Yongqin Xian (Postdoc)

Understanding how neural networks arrive at their predictions is essential for debugging, auditing, and deployment. Mechanistic interpretability pursues this goal by identifying circuits - minimal subnetworks responsible for specific behaviors. However, existing circuit discovery methods are brittle: circuits depend strongly on the chosen concept dataset and often fail to transfer out-of-distribution, raising doubts whether they capture concept or dataset-specific artifacts. We introduce Certified Circuits, which provide provable stability guarantees for circuit discovery. Our framework wraps any black-box discovery algorithm with randomized data subsampling to certify that circuit component inclusion decisions are invariant to bounded edit-distance perturbations of the concept dataset. Unstable neurons are abstained from, yielding circuits that are more compact and more accurate. On ImageNet and OOD datasets, certified circuits achieve up to 91% higher accuracy while using 45% fewer neurons, and remain reliable where baselines degrade. Certified Circuits puts circuit discovery on formal ground by producing mechanistic explanations that are provably stable and better aligned with the target concept. Code will be released soon!

Deep neural networks (DNNs) are widely used, but interpreting what they actually learn remains difficult. A major obstacle is that individual neurons often encode multiple unrelated concepts, obscuring the decision process of the network. While prior work, such as sparse autoencoders, can separate these mixed signals into more meaningful, "monosemantic" features, this typically requires altering the model in ways that can degrade downstream performance. To overcome this, we introduce ELUDe (explicit, lossless, unsupervised disentanglement), a method for improving the interpretability of DNNs while preserving their functional equivalence. ELUDe breaks latent representations into clear, inspectable sub-units that behave like interpretable features, while guaranteeing that the model's outputs remain exactly the same. It requires no explicit training, no labels, and can be applied to pretrained models. ELUDe works by reorganizing how information flows between layers, re-routing concept-specific contributions while preserving the original computation by construction. Across several vision models, including DINOv2 and supervised ViT-B/16, ELUDe improves interpretability, keeps downstream accuracy unchanged, runs efficiently, and supports practical uses such as steering model representations. In short, ELUDe offers interpretability (almost) without a tradeoff: clearer, scalable, and actionable model insights with no loss in performance.

Large Vision Language Models (LVLMs) have demonstrated remarkable capabilities, yet their proficiency in understanding and reasoning over multiple images remains largely unexplored. While existing benchmarks have initiated the evaluation of multi-image models, a comprehensive analysis of their core weaknesses and their causes is still lacking. In this work, we introduce MIMIC (Multi-Image Model Insights and Challenges), a new benchmark designed to rigorously evaluate the multi-image capabilities of LVLMs. Using MIMIC, we conduct a series of diagnostic experiments that reveal pervasive issues: LVLMs often fail to aggregate information across images and struggle to track or attend to multiple concepts simultaneously. To address these failures, we propose two novel complementary remedies. On the data side, we present a procedural data-generation strategy that composes single-image annotations into rich, targeted multi-image training examples. On the optimization side, we analyze layer-wise attention patterns and derive an attention-masking scheme tailored for multi-image inputs. Experiments substantially improved cross-image aggregation, while also enhancing performance on existing multi-image benchmarks, outperforming prior state of the art across tasks. Data and code will be made available at github.com/anurag-198/MIMIC.

Semantic segmentation requires dense pixel-level annotations, which are costly and time-consuming to acquire. To address this, we present SeSAM, a framework that uses a foundational segmentation model, i.e. Segment Anything Model (SAM), with weak labels, including coarse masks, scribbles, and points. SAM, originally designed for instance-based segmentation, cannot be directly used for semantic segmentation tasks. In this work, we identify specific challenges faced by SAM and determine appropriate components to adapt it for class-based segmentation using weak labels. Specifically, SeSAM decomposes class masks into connected components, samples point prompts along object skeletons, selects SAM masks using weak-label coverage, and iteratively refines labels using pseudo-labels, enabling SAM-generated masks to be effectively used for semantic segmentation. Integrated with a semi-supervised learning framework, SeSAM balances ground-truth labels, SAM-based pseudo-labels, and high-confidence pseudo-labels, significantly improving segmentation quality. Extensive experiments across multiple benchmarks and weak annotation types show that SeSAM consistently outperforms weakly supervised baselines while substantially reducing annotation cost relative to fine supervision.

We present Rewis3d, a framework that leverages recent advances in feed-forward 3D reconstruction to significantly improve weakly supervised semantic segmentation on 2D images. Obtaining dense, pixel-level annotations remains a costly bottleneck for training segmentation models. Alleviating this issue, sparse annotations offer an efficient weakly-supervised alternative. However, they still incur a performance gap. To address this, we introduce a novel approach that leverages 3D scene reconstruction as an auxiliary supervisory signal. Our key insight is that 3D geometric structure recovered from 2D videos provides strong cues that can propagate sparse annotations across entire scenes. Specifically, a dual student-teacher architecture enforces semantic consistency between 2D images and reconstructed 3D point clouds, using state-of-the-art feed-forward reconstruction to generate reliable geometric supervision. Extensive experiments demonstrate that Rewis3d achieves state-of-the-art performance in sparse supervision, outperforming existing approaches by 2-7% without requiring additional labels or inference overhead.

Most vision models are trained on RGB images processed through ISP pipelines optimized for human perception, which can discard sensor-level information useful for machine reasoning. RAW images preserve unprocessed scene data, enabling models to leverage richer cues for both object detection and object description, capturing fine-grained details, spatial relationships, and contextual information often lost in processed images. To support research in this domain, we introduce RAWDet-7, a large-scale dataset of ~25k training and 7.6k test RAW images collected across diverse cameras, lighting conditions, and environments, densely annotated for seven object categories following MS-COCO and LVIS conventions. In addition, we provide object-level descriptions derived from the corresponding high-resolution sRGB images, facilitating the study of object-level information preservation under RAW image processing and low-bit quantization. The dataset allows evaluation under simulated 4-bit, 6-bit, and 8-bit quantization, reflecting realistic sensor constraints, and provides a benchmark for studying detection performance, description quality & detail, and generalization in low-bit RAW image processing. Dataset & code upon acceptance.

Explainable artificial intelligence (XAI) aims to provide human-interpretable insights into the behavior of deep neural networks (DNNs), typically by estimating a simplified causal structure of the model. In existing work, this causal structure often includes relationships where the presence of a concept is associated with a strong activation of a neuron. For example, attribution methods primarily identify input pixels that contribute most to a prediction, and feature visualization methods reveal inputs that cause high activation of a target neuron - the former implicitly assuming that the relevant information resides in the input, and the latter that neurons encode the presence of concepts. However, a largely overlooked type of causal relationship is that of encoded absences, where the absence of a concept increases neural activation. In this work, we show that such missing but relevant concepts are common and that mainstream XAI methods struggle to reveal them when applied in their standard form. To address this, we propose two simple extensions to attribution and feature visualization techniques that uncover encoded absences. Across experiments, we show how mainstream XAI methods can be used to reveal and explain encoded absences, how ImageNet models exploit them, and that debiasing can be improved when considering them.

End-to-end autonomous driving has gained significant attention for its potential to learn robust behavior in interactive scenarios and scale with data. Popular architectures often build on separate modules for perception and planning connected through latent representations, such as bird's eye view feature grids, to maintain end-to-end differentiability. This paradigm emerged mostly on open-loop datasets, with evaluation focusing not only on driving performance, but also intermediate perception tasks. Unfortunately, architectural advances that excel in open-loop often fail to translate to scalable learning of robust closed-loop driving. In this paper, we systematically re-examine the impact of common architectural patterns on closed-loop performance: (1) high-resolution perceptual representations, (2) disentangled trajectory representations, and (3) generative planning. Crucially, our analysis evaluates the combined impact of these patterns, revealing both unexpected limitations as well as underexplored synergies. Building on these insights, we introduce BevAD, a novel lightweight and highly scalable end-to-end driving architecture. BevAD achieves 72.7% success rate on the Bench2Drive benchmark and demonstrates strong data-scaling behavior using pure imitation learning. Our code and models are publicly available here: dmholtz.github.io/bevad/

Large Vision-Language Models (LVLMs) map visual inputs into dense token sequences, imposing a quadratic computational bottleneck for inference. Elastic visual-token compression addresses this by training a single model that can run at multiple visual-token budgets. However, existing approaches struggle under aggressive compression. Spatial-only compression, as in nested pooling, behaves as an imperfect low-pass filter and induces spectral aliasing that obscures fine-grained detail. Query-only compression, as in nested query resampling, replaces explicit grid-aligned tokens with non-local summaries and substantially degrades spatial grounding. To resolve this representational conflict, we introduce PARCEL (Pool-Anchored Resampling with Conditioned Elastic Queries for Efficient Vision-Language Understanding), a visual tokenization architecture that dynamically partitions the labor of feature extraction. PARCEL establishes spatial pool tokens as low-frequency layout anchors and conditions elastic query tokens on these anchors through Pool-Conditioned Query Resampling. This encourages query tokens to focus on complementary visual features rather than redundant spatial mapping. Extensive evaluations across 27 benchmarks show that PARCEL improves the performance-efficiency Pareto frontier, consistently outperforming existing matryoshka baselines across visual-token budgets while preserving the "train once, deploy anywhere" paradigm.

Large vision-language models (LVLMs) tend to hallucinate, especially when visual inputs are corrupted at test time. We show that such corruptions act as additional distribution shifts, significantly amplifying hallucination rates in real-world applications. To address this, we propose CLIP-guided Test-Time Training (ClipTTT), a method to adapt LVLMs under degraded conditions on the fly with a single test sample. Specifically, we leverage the image-text alignment strength of a pre-trained CLIP model as a stable guidance signal to identify reliable self-supervision targets, enabling rapid adaptation without altering the base LVLMs. Extensive experiments on standard hallucination benchmarks, with 15 common corruptions, demonstrate that ClipTTT effectively mitigates hallucinations and improves descriptive faithfulness under visual corruptions.

Social media platforms have become primary arenas for climate communication, generating millions of images and posts that - if systematically analysed - can reveal which communication strategies mobilise public concern and which fall flat. We aim to facilitate such research by analysing how computer vision methods can be used for social media discourse analysis. This analysis includes application-based taxonomy design, model selection, prompt engineering, and validation. We benchmark six promptable vision-language models and 15 zero-shot CLIP-like models on two datasets from X (formerly Twitter) - a 1,038-image expert-annotated set and a larger corpus of over 1.2 million images, with 50,000 labels manually validated - spanning five annotation dimensions: animal content, climate change consequences, climate action, image setting, and image type. Among the models benchmarked, Gemini-3.1-flash-lite outperforms all others across all super-categories and both datasets, while the gap to open-weight models of moderate size remains relatively small. Beyond instance-level metrics, we advocate for distributional evaluation: VLM predictions can reliably recover population level trends even when per-image accuracy is moderate, making them a viable starting point for discourse analysis at scale. We find that chain-of-thought reasoning reduces rather than improves performance, and that annotation dimension specific prompt design improves performance. We release tweet IDs and labels along with our code at github.com/KathPra/Codebooks2VLMs.git.

Contrastive learning has become a fundamental approach in both uni-modal and multi-modal frameworks. This learning paradigm pulls positive pairs of samples closer while pushing negatives apart. In the uni-modal setting (e.g., image-based learning), previous research has shown that the strength of these forces can be controlled through the temperature parameter. In this work, we propose Multi-Modal Temperature and Margin Schedules (MM-TS), extending the concept of uni-modal temperature scheduling to multi-modal contrastive learning. Our method dynamically adjusts the temperature in the contrastive loss during training, modulating the attraction and repulsion forces in the multi-modal setting. Additionally, recognizing that standard multi-modal datasets often follow imbalanced, long-tail distributions, we adapt the temperature based on the local distribution of each training sample. Specifically, samples from dense clusters are assigned a higher temperature to better preserve their semantic structure. Furthermore, we demonstrate that temperature scheduling can be effectively integrated within a max-margin framework, thereby unifying the two predominant approaches in multi-modal contrastive learning: InfoNCE loss and max-margin objective. We evaluate our approach on four widely used image- and video-language datasets, Flickr30K, MSCOCO, EPIC-KITCHENS-100, and YouCook2, and show that our dynamic temperature and margin schedules improve performance and lead to new state-of-the-art results in the field.

Language-aligned vision foundation models perform strongly across diverse downstream tasks. Yet, their learned representations remain opaque, making interpreting their decision-making hard. Recent works decompose these representations into human-interpretable concepts, but provide poor spatial grounding and are limited to image classification tasks. In this work, we propose Insight, a language-aligned concept foundation model that provides fine-grained concepts, which are human-interpretable and spatially grounded in the input image. We leverage a hierarchical sparse autoencoder and a foundation model with strong semantic representations to automatically extract concepts at various granularities. Examining local co-occurrence dependencies of concepts allows us to define concept relationships. Through these relations we further improve concept naming and obtain richer explanations. On benchmark data, we show that Insight provides performance on classification and segmentation that is competitive with opaque foundation models while providing fine-grained, high quality concept-based explanations. Code is available at github.com/kawi19/Insight.

Vision Transformers (ViTs) have become a dominant architecture in computer vision, yet producing stable and high-resolution attribution maps for these models remains challenging. Architectural components such as patch embeddings and attention routing often introduce structured artifacts in pixel-level explanations, causing many existing methods to rely on coarse patch-level attributions. We introduce DAVE \textit{(\underline{D}istribution-aware \underline{A}ttribution via \underline{V}iT Gradient D\underline{E}composition)}, a mathematically grounded attribution method for ViTs based on a structured decomposition of the input gradient. By exploiting architectural properties of ViTs, DAVE isolates locally equivariant and stable components of the effective input--output mapping. It separates these from architecture-induced artifacts and other sources of instability.

Reinforcement learning (RL) with verifiable rewards (RLVR) has demonstrated the great potential of enhancing the reasoning abilities in multimodal large language models (MLLMs). However, the reliance on language-centric priors and expensive manual annotations prevents MLLMs' intrinsic visual understanding and scalable reward designs. In this work, we introduce SSL-R1, a generic self-supervised RL framework that derives verifiable rewards directly from images. To this end, we revisit self-supervised learning (SSL) in visual domains and reformulate widely-used SSL tasks into a set of verifiable visual puzzles for RL post-training, requiring neither human nor external model supervision. Training MLLMs on these tasks substantially improves their performance on multimodal understanding and reasoning benchmarks, highlighting the potential of leveraging vision-centric self-supervised tasks for MLLM post-training. We think this work will provide useful experience in devising effective self-supervised verifiable rewards to enable RL at scale. Project page: github.com/Jiahao000/SSL-R1.

Large vision-language models (LVLMs) have demonstrated impressive performance in various multimodal understanding and reasoning tasks. However, they still struggle with object hallucinations, i.e., the claim of nonexistent objects in the visual input. To address this challenge, we propose Region-aware Chain-of-Verification (R-CoV), a visual chain-of-verification method to alleviate object hallucinations in LVLMs in a post-hoc manner. Motivated by how humans comprehend intricate visual information -- often focusing on specific image regions or details within a given sample -- we elicit such region-level processing from LVLMs themselves and use it as a chaining cue to detect and alleviate their own object hallucinations. Specifically, our R-CoV consists of six steps: initial response generation, entity extraction, coordinate generation, region description, verification execution, and final response generation. As a simple yet effective method, R-CoV can be seamlessly integrated into various LVLMs in a training-free manner and without relying on external detection models. Extensive experiments on several widely used hallucination benchmarks across multiple LVLMs demonstrate that R-CoV can significantly alleviate object hallucinations in LVLMs. Project page: github.com/Jiahao000/R-CoV.

The pervasive growth of digital content, specifically short videos on social media platforms, has significantly altered how topics are discussed and understood in public discourse. In this work, we advance automated visual theme detection by assessing zero-shot and clustering capabilities on social media data. (1) We evaluated the capabilities of notable VLMs such as VideoChatGPT, PandaGPT, and VideoLLava using zero-shot image classification and compared their performance to the baseline provided by frame-wise CLIP image classification. (2) By treating clustering as a minimum cost multicut problem, we aim to uncover insightful patterns in an unsupervised manner. For both analysis strategies, we provide extensive evaluations and practical guidance to practitioners. While VLMs are currently not able to detect climate change specific classes, the clustering results are distinct visual frames. %Given that VLMs are not currently capable to grasp the climate change discourse, we focus the clustering evaluation of image embedding models. We find that both ConvNeXt V2 and DINOv2 produce meaningful clusters, with DINOv2 focusing more on style differences and abstract categories, while ConvNeXt V2 clusters differ in more fine-grained ways. Code available at github.com/KathPra/ClimateVID.git.

Transparency of neural networks' internal reasoning is at the heart of interpretability research, adding to trust, safety, and understanding of these models. The field of mechanistic interpretability has recently focused on studying task-specific computational graphs, defined by connections (edges) between model components. Such edge-based circuits have been defined in the context of large language models, yet vision-based approaches so far only consider neuron-based circuits. These tell which information is encoded, but not how it is routed through the complex wiring of a neural network. In this work, we investigate whether useful mechanistic circuits can be identified through computational graphs in vision transformers. We propose an effective method for Automatic Visual Circuit Discovery (Vi-CD) that recovers class-specific circuits for classification, identifies circuits underlying typographic attacks in CLIP, and discovers circuits that lend themselves for steering to correct harmful model behavior. Overall, we find that insightful and actionable edge-based circuits can be recovered from vision transformers, adding transparency to the internal computations of these models.

Autoregressive (AR) models have achieved remarkable success in natural
language and image generation, but their application to 3D shape modeling
remains largely unexplored. Unlike diffusion models, AR models enable more
efficient and controllable generation with faster inference times, making them
especially suitable for data-intensive domains. Traditional 3D generative
models using AR approaches often rely on ``next-token" predictions at the voxel
or point level. While effective for certain applications, these methods can be
restrictive and computationally expensive when dealing with large-scale 3D
data. To tackle these challenges, we introduce 3D-WAG, an AR model for 3D
implicit distance fields that can perform unconditional shape generation,
class-conditioned and also text-conditioned shape generation. Our key idea is
to encode shapes as multi-scale wavelet token maps and use a Transformer to
predict the ``next higher-resolution token map" in an autoregressive manner. By
redefining 3D AR generation task as ``next-scale" prediction, we reduce the
computational cost of generation compared to traditional ``next-token"
prediction models, while preserving essential geometric details of 3D shapes in
a more structured and hierarchical manner. We evaluate 3D-WAG to showcase its
benefit by quantitative and qualitative comparisons with state-of-the-art
methods on widely used benchmarks. Our results show 3D-WAG achieves superior
performance in key metrics like Coverage and MMD, generating high-fidelity 3D
shapes that closely match the real data distribution.

State-of-the-art novel view synthesis methods achieve impressive results for
multi-view captures of static 3D scenes. However, the reconstructed scenes
still lack "liveliness," a key component for creating engaging 3D experiences.
Recently, novel video diffusion models generate realistic videos with complex
motion and enable animations of 2D images, however they cannot naively be used
to animate 3D scenes as they lack multi-view consistency. To breathe life into
the static world, we propose Gaussians2Life, a method for animating parts of
high-quality 3D scenes in a Gaussian Splatting representation. Our key idea is
to leverage powerful video diffusion models as the generative component of our
model and to combine these with a robust technique to lift 2D videos into
meaningful 3D motion. We find that, in contrast to prior work, this enables
realistic animations of complex, pre-existing 3D scenes and further enables the
animation of a large variety of object classes, while related work is mostly
focused on prior-based character animation, or single 3D objects. Our model
enables the creation of consistent, immersive 3D experiences for arbitrary
scenes.

We introduce PersonaHOI, a training- and tuning-free framework that fuses a
general StableDiffusion model with a personalized face diffusion (PFD) model to
generate identity-consistent human-object interaction (HOI) images. While
existing PFD models have advanced significantly, they often overemphasize
facial features at the expense of full-body coherence, PersonaHOI introduces an
additional StableDiffusion (SD) branch guided by HOI-oriented text inputs. By
incorporating cross-attention constraints in the PFD branch and spatial merging
at both latent and residual levels, PersonaHOI preserves personalized facial
details while ensuring interactive non-facial regions. Experiments, validated
by a novel interaction alignment metric, demonstrate the superior realism and
scalability of PersonaHOI, establishing a new standard for practical
personalized face with HOI generation. Our code will be available at
github.com/JoyHuYY1412/PersonaHOI

Deep neural networks (DNNs) have proven to be successful in various computer
vision applications such that models even infer in safety-critical situations.
Therefore, vision models have to behave in a robust way to disturbances such as
noise or blur. While seminal benchmarks exist to evaluate model robustness to
diverse corruptions, blur is often approximated in an overly simplistic way to
model defocus, while ignoring the different blur kernel shapes that result from
optical systems. To study model robustness against realistic optical blur
effects, this paper proposes two datasets of blur corruptions, which we denote
OpticsBench and LensCorruptions. OpticsBench examines primary aberrations such
as coma, defocus, and astigmatism, i.e. aberrations that can be represented by
varying a single parameter of Zernike polynomials. To go beyond the principled
but synthetic setting of primary aberrations, LensCorruptions samples linear
combinations in the vector space spanned by Zernike polynomials, corresponding
to 100 real lenses. Evaluations for image classification and object detection
on ImageNet and MSCOCO show that for a variety of different pre-trained models,
the performance on OpticsBench and LensCorruptions varies significantly,
indicating the need to consider realistic image corruptions to evaluate a
model's robustness against blur.

An important challenge when using computer vision models in the real world is to evaluate their performance in potential out-of-distribution (OOD) scenarios. While simple synthetic corruptions are commonly applied to test OOD robustness, they often fail to capture nuisance shifts that occur in the real world. Recently, diffusion models have been applied to generate realistic images for benchmarking, but they are restricted to binary nuisance shifts. In this work, we introduce CNS-Bench, a Continuous Nuisance Shift Benchmark to quantify OOD robustness of image classifiers for continuous and realistic generative nuisance shifts. CNS-Bench allows generating a wide range of individual nuisance shifts in continuous severities by applying LoRA adapters to diffusion models. To address failure cases, we propose a filtering mechanism that outperforms previous methods, thereby enabling reliable benchmarking with generative models. With the proposed benchmark, we perform a large-scale study to evaluate the robustness of more than 40 classifiers under various nuisance shifts. Through carefully designed comparisons and analyses, we find that model rankings can change for varying shifts and shift scales, which cannot be captured when applying common binary shifts. Additionally, we show that evaluating the model performance on a continuous scale allows the identification of model failure points, providing a more nuanced understanding of model robustness. Project page including code and data: genintel.github.io/CNS.

Backgrounds in images play a major role in contributing to spurious correlations among different data points. Owing to aesthetic preferences of humans capturing the images, datasets can exhibit positional (location of the object within a given frame) and size (region-of-interest to image ratio) biases for different classes. In this paper, we show that these biases can impact how much a model relies on spurious features in the background to make its predictions. To better illustrate our findings, we propose a synthetic dataset derived from ImageNet1k, Hard-Spurious-ImageNet, which contains images with various backgrounds, object positions, and object sizes. By evaluating the dataset on different pretrained models, we find that most models rely heavily on spurious features in the background when the region-of-interest (ROI) to image ratio is small and the object is far from the center of the image. Moreover, we also show that current methods that aim to mitigate harmful spurious features, do not take into account these factors, hence fail to achieve considerable performance gains for worst-group accuracies when the size and location of core features in an image change.

Open-weight LLMs can be modified at inference time with simple activation edits, which raises a practical question for safety: do common safety interventions like refusal training or metatag training survive such edits? We study model abliteration, a lightweight projection technique designed to remove refusal-sensitive directions, and conduct a controlled evaluation across a granular sequence of Safety Pretraining checkpoints for SmolLM2-1.7B, alongside widely used open baselines. For each of 20 systems, original and abliterated, we issue 100 prompts with balanced harmful and harmless cases, classify responses as **Refusal** or **Non-Refusal** using multiple judges, and validate judge fidelity on a small human-labeled subset. We also probe whether models can identify refusal in their own outputs. Our study produces a checkpoint-level characterization of which data-centric safety components remain robust under abliteration, quantifies how judge selection influences evaluation outcomes, and outlines a practical protocol for integrating inference-time edits into safety assessments. Code: github.com/shashankskagnihotri/safety_pretraining.

Deep learning (DL) has surpassed human performance on standard benchmarks, driving its widespread adoption in computer vision tasks. One such task is disparity estimation, estimating the disparity between matching pixels in stereo image pairs, which is crucial for safety-critical applications like medical surgeries and autonomous navigation. However, DL-based disparity estimation methods are highly susceptible to distribution shifts and adversarial attacks, raising concerns about their reliability and generalization. Despite these concerns, a standardized benchmark for evaluating the robustness of disparity estimation methods remains absent, hindering progress in the field.
To address this gap, we introduce DispBench, a comprehensive benchmarking tool for systematically assessing the reliability of disparity estimation methods. DispBench evaluates robustness against synthetic image corruptions such as adversarial attacks and out-of-distribution shifts caused by 2D Common Corruptions across multiple datasets and diverse corruption scenarios. We conduct the most extensive performance and robustness analysis of disparity estimation methods to date, uncovering key correlations between accuracy, reliability, and generalization. Open-source code for DispBench: github.com/shashankskagnihotri/benchmarking_robustness/tree/disparity_estimation/final/disparity_estimation

Reliability and generalization in deep learning are predominantly studied in the context of image classification. Yet, real-world applications in safety-critical domains involve a broader set of semantic tasks, such as semantic segmentation and object detection, which come with a diverse set of dedicated model architectures. To facilitate research towards robust model design in segmentation and detection, our primary objective is to provide benchmarking tools regarding robustness to distribution shifts and adversarial manipulations. We propose the benchmarking tools SEMSEGBENCH and DETECBENCH, along with the most extensive evaluation to date on the reliability and generalization of semantic segmentation and object detection models. In particular, we benchmark 76 segmentation models across four datasets and 61 object detectors across two datasets, evaluating their performance under diverse adversarial attacks and common corruptions. Our findings reveal systematic weaknesses in state-of-the-art models and uncover key trends based on architecture, backbone, and model capacity. SEMSEGBENCH and DETECBENCH are open-sourced in our GitHub repository (https://github.com/shashankskagnihotri/benchmarking_reliability_generalization) along with our complete set of total 6139 evaluations. We anticipate the collected data to foster and encourage future research towards improved model reliability beyond classification.

Finding correspondences between semantically similar points across images and object instances is one of the everlasting challenges in computer vision. While large pre-trained vision models have recently been demonstrated as effective priors for semantic matching, they still suffer from ambiguities for symmetric objects or repeated object parts. We propose to improve semantic correspondence estimation via 3D-aware pseudo-labeling. Specifically, we train an adapter to refine off-the-shelf features using pseudo-labels obtained via 3D-aware chaining, filtering wrong labels through relaxed cyclic consistency, and 3D spherical prototype mapping constraints. While reducing the need for dataset specific annotations compared to prior work, we set a new state-of-the-art on SPair-71k by over 4% absolute gain and by over 7% against methods with similar supervision requirements. The generality of our proposed approach simplifies extension of training to other data sources, which we demonstrate in our experiments.

Flow-based latent generative models such as Stable Diffusion 3 are able to
generate images with remarkable quality, even enabling photorealistic
text-to-image generation. Their impressive performance suggests that these
models should also constitute powerful priors for inverse imaging problems, but
that approach has not yet led to comparable fidelity. There are several key
obstacles: (i) the encoding into a lower-dimensional latent space makes the
underlying (forward) mapping non-linear; (ii) the data likelihood term is
usually intractable; and (iii) learned generative models struggle to recover
rare, atypical data modes during inference. We present FLAIR, a novel training
free variational framework that leverages flow-based generative models as a
prior for inverse problems. To that end, we introduce a variational objective
for flow matching that is agnostic to the type of degradation, and combine it
with deterministic trajectory adjustments to recover atypical modes. To enforce
exact consistency with the observed data, we decouple the optimization of the
data fidelity and regularization terms. Moreover, we introduce a time-dependent
calibration scheme in which the strength of the regularization is modulated
according to off-line accuracy estimates. Results on standard imaging
benchmarks demonstrate that FLAIR consistently outperforms existing diffusion-
and flow-based methods in terms of reconstruction quality and sample diversity.

PyG (PyTorch Geometric) has evolved significantly since its initial release, establishing itself as a leading framework for Graph Neural Networks. In this paper, we present Pyg 2.0 (and its subsequent minor versions), a comprehensive update that introduces substantial improvements in scalability and real-world application capabilities. We detail the framework's enhanced architecture, including support for heterogeneous and temporal graphs, scalable feature/graph stores, and various optimizations, enabling researchers and practitioners to tackle large-scale graph learning problems efficiently. Over the recent years, PyG has been supporting graph learning in a large variety of application areas, which we will summarize, while providing a deep dive into the important areas of relational deep learning and large language modeling.

Neural networks are widely adopted to solve complex and challenging tasks.
Especially in high-stakes decision-making, understanding their reasoning
process is crucial, yet proves challenging for modern deep networks. Feature
visualization (FV) is a powerful tool to decode what information neurons are
responding to and hence to better understand the reasoning behind such
networks. In particular, in FV we generate human-understandable images that
reflect the information detected by neurons of interest. However, current
methods often yield unrecognizable visualizations, exhibiting repetitive
patterns and visual artifacts that are hard to understand for a human. To
address these problems, we propose to guide FV through statistics of real image
features combined with measures of relevant network flow to generate
prototypical images. Our approach yields human-understandable visualizations
that both qualitatively and quantitatively improve over state-of-the-art FVs
across various architectures. As such, it can be used to decode which
information the network uses, complementing mechanistic circuits that identify
where it is encoded. Code is available at: github.com/adagorgun/VITAL

Diffusion models are usually evaluated by their final outputs, gradually denoising random noise into meaningful images. Yet, generation unfolds along a trajectory, and analyzing this dynamic process is crucial for understanding how controllable, reliable, and predictable these models are in terms of their success/failure modes. In this work, we ask the question: when does noise turn into a specific concept (e.g., age) and lock in the denoising trajectory? We propose PCI (Prompt-Conditioned Intervention) to study this question. PCI is a training-free and model-agnostic framework for analyzing concept dynamics through diffusion time. The central idea is the analysis of Concept Insertion Success (CIS), defined as the probability that a concept inserted at a given timestep is preserved and reflected in the final image, offering a way to characterize the temporal dynamics of concept formation. Applied to several state-of-the-art text-to-image diffusion models and a broad taxonomy of concepts, PCI reveals diverse temporal behaviors across diffusion models, in which certain phases of the trajectory are more favorable to specific concepts even within the same concept type. These findings also provide actionable insights for text-driven image editing, highlighting when interventions are most effective without requiring access to model internals or training, and yielding quantitatively stronger edits that achieve a balance of semantic accuracy and content preservation than strong baselines. Code is available at: github.com/adagorgun/PCI-Prompt-Controlled-Interventions

Deep learning has become an essential part of computer vision, with deep
neural networks (DNNs) excelling in predictive performance. However, they often
fall short in other critical quality dimensions, such as robustness,
calibration, or fairness. While existing studies have focused on a subset of
these quality dimensions, none have explored a more general form of
"well-behavedness" of DNNs. With this work, we address this gap by
simultaneously studying nine different quality dimensions for image
classification. Through a large-scale study, we provide a bird's-eye view by
analyzing 326 backbone models and how different training paradigms and model
architectures affect the quality dimensions. We reveal various new insights
such that (i) vision-language models exhibit high fairness on ImageNet-1k
classification and strong robustness against domain changes; (ii)
self-supervised learning is an effective training paradigm to improve almost
all considered quality dimensions; and (iii) the training dataset size is a
major driver for most of the quality dimensions. We conclude our study by
introducing the QUBA score (Quality Understanding Beyond Accuracy), a novel
metric that ranks models across multiple dimensions of quality, enabling
tailored recommendations based on specific user needs.

The wide availability and low usability barrier of modern image generation models has triggered the reasonable fear of criminal misconduct and negative social implications. The machine learning community has been engaging this problem with an extensive series of publications proposing algorithmic solutions for the detection of "fake", e.g. entirely generated or partially manipulated images. While there is undoubtedly some progress towards technical solutions of the problem, we argue that current and prior work is focusing too much on generative algorithms and "fake" data-samples, neglecting a clear definition and data collection of "real" images. The fundamental question "what is a real image?" might appear to be quite philosophical, but our analysis shows that the development and evaluation of basically all current "fake"-detection methods is relying on only a few, quite old low-resolution datasets of "real" images like ImageNet. However, the technology for the acquisition of "real" images, aka taking photos, has drastically evolved over the last decade: Today, over 90% of all photographs are produced by smartphones which typically use algorithms to compute an image from multiple inputs (over time) from multiple sensors. Based on the fact that these image formation algorithms are typically neural network architectures which are closely related to "fake"-image generators, we state the position that today, we need to re-think the concept of "real" images. The purpose of this position paper is to raise the awareness of the current shortcomings in this active field of research and to trigger an open discussion whether the detection of "fake" images is a sound objective at all. At the very least, we need a clear technical definition of "real" images and new benchmark datasets.

Faithfulness and interpretability are essential for deploying deep neural networks (DNNs) in safety-critical domains such as medical imaging. B-cos networks offer a promising solution by replacing standard linear layers with a weight-input alignment mechanism, producing inherently interpretable, class-specific explanations without post-hoc methods. While maintaining diagnostic performance competitive with state-of-the-art DNNs, standard B-cos models suffer from severe aliasing artifacts in their explanation maps, making them unsuitable for clinical use where clarity is essential. In this work, we address these limitations by introducing anti-aliasing strategies using FLCPooling (FLC) and BlurPool (BP) to significantly improve explanation quality. Our experiments on chest X-ray datasets demonstrate that the modified $\text{B-cos}_\text{FLC}$ and $\text{B-cos}_\text{BP}$ preserve strong predictive performance while providing faithful and artifact-free explanations suitable for clinical application in multi-class and multi-label settings. Code available at: GitHub repository (url: github.com/mkleinma/B-cos-medical-paper).

Most existing approaches to referring segmentation achieve strong performance only through fine-tuning or by composing multiple pre-trained models, often at the cost of additional training and architectural modifications. Meanwhile, large-scale generative diffusion models encode rich semantic information, making them attractive as general-purpose feature extractors. In this work, we introduce a new method that directly exploits features, attention scores, from diffusion transformers for downstream tasks, requiring neither architectural modifications nor additional training. To systematically evaluate these features, we extend benchmarks with vision-language grounding tasks spanning both images and videos. Our key insight is that stop words act as attention magnets: they accumulate surplus attention and can be filtered to reduce noise. Moreover, we identify global attention sinks (GAS) emerging in deeper layers and show that they can be safely suppressed or redirected onto auxiliary tokens, leading to sharper and more accurate grounding maps. We further propose an attention redistribution strategy, where appended stop words partition background activations into smaller clusters, yielding sharper and more localized heatmaps. Building on these findings, we develop RefAM, a simple training-free grounding framework that combines cross-attention maps, GAS handling, and redistribution. Across zero-shot referring image and video segmentation benchmarks, our approach consistently outperforms prior methods, establishing a new state of the art without fine-tuning or additional components.

The integration of Large Language Model (LLMs) blocks with Vision Transformers (ViTs) holds immense promise for vision-only tasks by leveraging the rich semantic knowledge and reasoning capabilities of LLMs. However, a fundamental challenge lies in the inherent modality mismatch between text-centric pretraining of LLMs and vision-centric training of ViTs. Direct fusion often fails to fully exploit the LLM's potential and suffers from unstable finetuning. As a result, LLM blocks are kept frozen while only the vision components are learned. As a remedy to these challenges, we introduce Language-Unlocked Vision Transformers (LUViT), a novel approach that bridges this modality mismatch through a synergistic pre-training strategy. LUViT co-adapts a ViT backbone and an LLM fusion block by (1) employing Masked Auto-Encoding (MAE) to pre-train the ViT for richer visual representations, and (2) concurrently training Low-Rank Adaptation (LoRA) layers within the LLM block using the MAE objective. This joint optimization guides the ViT to produce LLM-aligned features and the LLM to effectively interpret visual information. We demonstrate through extensive experiments that LUViT significantly improves performance on various downstream vision tasks, showcasing a more effective and efficient pathway to harness LLM knowledge for visual understanding.

The assessment of evaluation metrics (meta-evaluation) is crucial for determining the suitability of existing metrics in text-to-image (T2I) generation tasks. Human-based meta-evaluation is costly and time-intensive, and automated alternatives are scarce. We address this gap and propose CROC: a scalable framework for automated Contrastive Robustness Checks that systematically probes and quantifies metric robustness by synthesizing contrastive test cases across a comprehensive taxonomy of image properties. With CROC, we generate a pseudo-labeled dataset (CROC$^{syn}$) of over one million contrastive prompt-image pairs to enable a fine-grained comparison of evaluation metrics. We also use the dataset to train CROCScore, a new metric that achieves state-of-the-art performance among open-source methods, demonstrating an additional key application of our framework. To complement this dataset, we introduce a human-supervised benchmark (CROC$^{hum}$) targeting especially challenging categories. Our results highlight robustness issues in existing metrics: for example, many fail on prompts involving negation, and all tested open-source metrics fail on at least 25% of cases involving correct identification of body parts.

Adversarial Training (AT) is a widely adopted defense against adversarial examples. However, existing approaches typically apply a uniform training objective across all classes, overlooking disparities in class-wise vulnerability. This results in adversarial unfairness: classes with well distinguishable features (strong classes) tend to become more robust, while classes with overlapping or shared features(weak classes) remain disproportionately susceptible to adversarial attacks. We observe that strong classes do not require strong adversaries during training, as their non-robust features are quickly suppressed. In contrast, weak classes benefit from stronger adversaries to effectively reduce their vulnerabilities. Motivated by this, we introduce TRIX, a feature-aware adversarial training framework that adaptively assigns weaker targeted adversaries to strong classes, promoting feature diversity via uniformly sampled targets, and stronger untargeted adversaries to weak classes, enhancing their focused robustness. TRIX further incorporates per-class loss weighting and perturbation strength adjustments, building on prior work, to emphasize weak classes during the optimization. Comprehensive experiments on standard image classification benchmarks, including evaluations under strong attacks such as PGD and AutoAttack, demonstrate that TRIX significantly improves worst-case class accuracy on both clean and adversarial data, reducing inter-class robustness disparities, and preserves overall accuracy. Our results highlight TRIX as a practical step toward fair and effective adversarial defense.

Latent generative models have shown remarkable progress in high-fidelity image synthesis, typically using a two-stage training process that involves compressing images into latent embeddings via learned tokenizers in the first stage. The quality of generation strongly depends on how expressive and well-optimized these latent embeddings are. While various methods have been proposed to learn effective latent representations, generated images often lack realism, particularly in textured regions with sharp transitions, due to loss of fine details governed by high frequencies. We conduct a detailed frequency decomposition of existing state-of-the-art (SOTA) latent tokenizers and show that conventional objectives inherently prioritize low-frequency reconstruction, often at the expense of high-frequency fidelity. Our analysis reveals these latent tokenizers exhibit a bias toward low-frequency information during optimization, leading to over-smoothed outputs and visual artifacts that diminish perceptual quality. To address this, we propose a wavelet-based, frequency-aware variational autoencoder (FA-VAE) framework that explicitly decouples the optimization of low- and high-frequency components. This decoupling enables improved reconstruction of fine textures while preserving global structure. Moreover, we integrate our frequency-preserving latent embeddings into a SOTA latent diffusion model, resulting in sharper and more realistic image generation. Our approach bridges the fidelity gap in current latent tokenizers and emphasizes the importance of frequency-aware optimization for realistic image synthesis, with broader implications for applications in content creation, neural rendering, and medical imaging.

With the rise of neural networks, especially in high-stakes applications,
these networks need two properties (i) robustness and (ii) interpretability to
ensure their safety. Recent advances in classifiers with 3D volumetric object
representations have demonstrated a greatly enhanced robustness in
out-of-distribution data. However, these 3D-aware classifiers have not been
studied from the perspective of interpretability. We introduce CAVE - Concept
Aware Volumes for Explanations - a new direction that unifies interpretability
and robustness in image classification. We design an inherently-interpretable
and robust classifier by extending existing 3D-aware classifiers with concepts
extracted from their volumetric representations for classification. In an array
of quantitative metrics for interpretability, we compare against different
concept-based approaches across the explainable AI literature and show that
CAVE discovers well-grounded concepts that are used consistently across images,
while achieving superior robustness.

Accurate monocular metric depth estimation (MMDE) is crucial to solving
downstream tasks in 3D perception and modeling. However, the remarkable
accuracy of recent MMDE methods is confined to their training domains. These
methods fail to generalize to unseen domains even in the presence of moderate
domain gaps, which hinders their practical applicability. We propose a new
model, UniDepthV2, capable of reconstructing metric 3D scenes from solely
single images across domains. Departing from the existing MMDE paradigm,
UniDepthV2 directly predicts metric 3D points from the input image at inference
time without any additional information, striving for a universal and flexible
MMDE solution. In particular, UniDepthV2 implements a self-promptable camera
module predicting a dense camera representation to condition depth features.
Our model exploits a pseudo-spherical output representation, which disentangles
the camera and depth representations. In addition, we propose a geometric
invariance loss that promotes the invariance of camera-prompted depth features.
UniDepthV2 improves its predecessor UniDepth model via a new edge-guided loss
which enhances the localization and sharpness of edges in the metric depth
outputs, a revisited, simplified and more efficient architectural design, and
an additional uncertainty-level output which enables downstream tasks requiring
confidence. Thorough evaluations on ten depth datasets in a zero-shot regime
consistently demonstrate the superior performance and generalization of
UniDepthV2. Code and models are available at
github.com/lpiccinelli-eth/UniDepth

Monocular 3D estimation is crucial for visual perception. However, current
methods fall short by relying on oversimplified assumptions, such as pinhole
camera models or rectified images. These limitations severely restrict their
general applicability, causing poor performance in real-world scenarios with
fisheye or panoramic images and resulting in substantial context loss. To
address this, we present UniK3D, the first generalizable method for monocular
3D estimation able to model any camera. Our method introduces a spherical 3D
representation which allows for better disentanglement of camera and scene
geometry and enables accurate metric 3D reconstruction for unconstrained camera
models. Our camera component features a novel, model-independent representation
of the pencil of rays, achieved through a learned superposition of spherical
harmonics. We also introduce an angular loss, which, together with the camera
module design, prevents the contraction of the 3D outputs for wide-view
cameras. A comprehensive zero-shot evaluation on 13 diverse datasets
demonstrates the state-of-the-art performance of UniK3D across 3D, depth, and
camera metrics, with substantial gains in challenging large-field-of-view and
panoramic settings, while maintaining top accuracy in conventional pinhole
small-field-of-view domains. Code and models are available at
github.com/lpiccinelli-eth/unik3d .

Terahertz (THz) imaging enables non-invasive analysis for applications such as security screening and material classification, but effective image classification remains challenging due to limited annotations, low resolution, and visual ambiguity. We introduce In-Context Learning (ICL) with Vision-Language Models (VLMs) as a flexible, interpretable alternative that requires no fine-tuning. Using a modality-aligned prompting framework, we adapt two open-weight VLMs to the THz domain and evaluate them under zero-shot and one-shot settings. Our results show that ICL improves classification and interpretability in low-data regimes. This is the first application of ICL-enhanced VLMs to THz imaging, offering a promising direction for resource-constrained scientific domains. Code: \href{https://github.com/Nicolas-Poggi/Project_THz_Classification/tree/main}{GitHub repository}.

Climate change is one of the most pressing challenges of the 21st century,
sparking widespread discourse across social media platforms. Activists,
policymakers, and researchers seek to understand public sentiment and
narratives while access to social media data has become increasingly restricted
in the post-API era. In this study, we analyze a dataset of climate
change-related tweets from X (formerly Twitter) shared in 2019, containing 730k
tweets along with the shared images. Our approach integrates statistical
analysis, image classification, object detection, and sentiment analysis to
explore visual narratives in climate discourse. Additionally, we introduce a
graphical user interface (GUI) to facilitate interactive data exploration. Our
findings reveal key themes in climate communication, highlight sentiment
divergence between images and text, and underscore the strengths and
limitations of foundation models in analyzing social media imagery. By
releasing our code and tools, we aim to support future research on the
intersection of climate change, social media, and computer vision.

Open-vocabulary semantic segmentation models associate vision and text to
label pixels from an undefined set of classes using textual queries, providing
versatile performance on novel datasets. However, large shifts between training
and test domains degrade their performance, requiring fine-tuning for effective
real-world applications. We introduce Semantic Library Adaptation (SemLA), a
novel framework for training-free, test-time domain adaptation. SemLA leverages
a library of LoRA-based adapters indexed with CLIP embeddings, dynamically
merging the most relevant adapters based on proximity to the target domain in
the embedding space. This approach constructs an ad-hoc model tailored to each
specific input without additional training. Our method scales efficiently,
enhances explainability by tracking adapter contributions, and inherently
protects data privacy, making it ideal for sensitive applications.
Comprehensive experiments on a 20-domain benchmark built over 10 standard
datasets demonstrate SemLA's superior adaptability and performance across
diverse settings, establishing a new standard in domain adaptation for
open-vocabulary semantic segmentation.

Standard benchmarks for optical flow, scene flow, and stereo vision algorithms generally focus on model accuracy rather than robustness to image corruptions like noise or rain. Hence, the resilience of models to such real-world perturbations is largely unquantified. To address this, we present RobustSpring, a comprehensive dataset and benchmark for evaluating robustness to image corruptions for optical flow, scene flow, and stereo models. RobustSpring applies 20 different image corruptions, including noise, blur, color changes, quality degradations, and weather distortions, in a time-, stereo-, and depth-consistent manner to the high-resolution Spring dataset, creating a suite of 20,000 corrupted images that reflect challenging conditions. RobustSpring enables comparisons of model robustness via a new corruption robustness metric. Integration with the Spring benchmark enables public two-axis evaluations of both accuracy and robustness. We benchmark a curated selection of initial models, observing that accurate models are not necessarily robust and that robustness varies widely by corruption type. RobustSpring is a new computer vision benchmark that treats robustness as a first-class citizen to foster models that combine accuracy with resilience. It will be available at spring-benchmark.org.

Inspired by the human visual system's mechanisms for contrast enhancement and color-opponency, we explore biologically motivated input preprocessing for robust semantic segmentation. By applying Difference-of-Gaussians (DoG) filtering to RGB, grayscale, and opponent-color channels, we enhance local contrast without modifying model architecture or training. Evaluations on Cityscapes, ACDC, and Dark Zurich show that such preprocessing maintains in-distribution performance while improving robustness to adverse conditions like night, fog, and snow. As this processing is model-agnostic and lightweight, it holds potential for integration into imaging pipelines, enabling imaging systems to deliver task-ready, robust inputs for downstream vision models in safety-critical environments.

We introduce Spatial Reasoning Models (SRMs), a framework to perform
reasoning over sets of continuous variables via denoising generative models.
SRMs infer continuous representations on a set of unobserved variables, given
observations on observed variables. Current generative models on spatial
domains, such as diffusion and flow matching models, often collapse to
hallucination in case of complex distributions. To measure this, we introduce a
set of benchmark tasks that test the quality of complex reasoning in generative
models and can quantify hallucination. The SRM framework allows to report key
findings about importance of sequentialization in generation, the associated
order, as well as the sampling strategies during training. It demonstrates, for
the first time, that order of generation can successfully be predicted by the
denoising network itself. Using these findings, we can increase the accuracy of
specific reasoning tasks from 1% to >50%.

We introduce AnyUp, a method for feature upsampling that can be applied to any vision feature at any resolution, without encoder-specific training. Existing learning-based upsamplers for features like DINO or CLIP need to be re-trained for every feature extractor and thus do not generalize to different feature types at inference time. In this work, we propose an inference-time feature-agnostic upsampling architecture to alleviate this limitation and improve upsampling quality. In our experiments, AnyUp sets a new state of the art for upsampled features, generalizes to different feature types, and preserves feature semantics while being efficient and easy to apply to a wide range of downstream tasks.

Recent advances in multi-modal generative models have enabled significant
progress in instruction-based image editing. However, while these models
produce visually plausible outputs, their capacity for knowledge-based
reasoning editing tasks remains under-explored. In this paper, we introduce
KRIS-Bench (Knowledge-based Reasoning in Image-editing Systems Benchmark), a
diagnostic benchmark designed to assess models through a cognitively informed
lens. Drawing from educational theory, KRIS-Bench categorizes editing tasks
across three foundational knowledge types: Factual, Conceptual, and Procedural.
Based on this taxonomy, we design 22 representative tasks spanning 7 reasoning
dimensions and release 1,267 high-quality annotated editing instances. To
support fine-grained evaluation, we propose a comprehensive protocol that
incorporates a novel Knowledge Plausibility metric, enhanced by knowledge hints
and calibrated through human studies. Empirical results on 10 state-of-the-art
models reveal significant gaps in reasoning performance, highlighting the need
for knowledge-centric benchmarks to advance the development of intelligent
image editing systems.

We propose MVGBench, a comprehensive benchmark for multi-view image generation models (MVGs) that evaluates 3D consistency in geometry and texture, image quality, and semantics (using vision language models). Recently, MVGs have been the main driving force in 3D object creation. However, existing metrics compare generated images against ground truth target views, which is not suitable for generative tasks where multiple solutions exist while differing from ground truth. Furthermore, different MVGs are trained on different view angles, synthetic data and specific lightings -- robustness to these factors and generalization to real data are rarely evaluated thoroughly. Without a rigorous evaluation protocol, it is also unclear what design choices contribute to the progress of MVGs. MVGBench evaluates three different aspects: best setup performance, generalization to real data and robustness. Instead of comparing against ground truth, we introduce a novel 3D self-consistency metric which compares 3D reconstructions from disjoint generated multi-views. We systematically compare 12 existing MVGs on 4 different curated real and synthetic datasets. With our analysis, we identify important limitations of existing methods specially in terms of robustness and generalization, and we find the most critical design choices. Using the discovered best practices, we propose ViFiGen, a method that outperforms all evaluated MVGs on 3D consistency. Our code, model, and benchmark suite will be publicly released.

Open set recognition (OSR) is devised to address the problem of detecting novel classes during model inference. Even in recent vision models, this remains an open issue which is receiving increasing attention. Thereby, a crucial challenge is to learn features that are relevant for unseen categories from given data, for which these features might not be discriminative. To facilitate this process and "optimize to learn" more diverse features, we propose GradMix, a data augmentation method that dynamically leverages gradient-based attribution maps of the model during training to mask out already learned concepts. Thus GradMix encourages the model to learn a more complete set of representative features from the same data source. Extensive experiments on open set recognition, close set classification, and out-of-distribution detection reveal that our method can often outperform the state-of-the-art. GradMix can further increase model robustness to corruptions as well as downstream classification performance for self-supervised learning, indicating its benefit for model generalization.

Large language models (LLMs) excel at natural language understanding and generation but remain vulnerable to factual errors, limiting their reliability in knowledge-intensive tasks. While decoding-time strategies provide a promising efficient solution without training, existing methods typically treat token-level and layer-level signals in isolation, overlooking the joint dynamics between them. In this work, we introduce a token-aware, layer-localized contrastive decoding method that aligns specific token types with their most influential transformer layers to improve factual generation. Through empirical attention analysis, we identify two key patterns: punctuation tokens receive dominant attention in early layers, while conceptual tokens govern semantic reasoning in intermediate layers. By selectively suppressing attention to these token types at their respective depths, we achieve the induction of controlled factual degradation and derive contrastive signals to guide the final factual decoding. Our method requires no additional training or model modification, and experiments demonstrate that our method consistently improves factuality across multiple LLMs and various benchmarks.

B-cos Networks have been shown to be effective for obtaining highly human
interpretable explanations of model decisions by architecturally enforcing
stronger alignment between inputs and weight. B-cos variants of convolutional
networks (CNNs) and vision transformers (ViTs), which primarily replace linear
layers with B-cos transformations, perform competitively to their respective
standard variants while also yielding explanations that are faithful by design.
However, it has so far been necessary to train these models from scratch, which
is increasingly infeasible in the era of large, pre-trained foundation models.
In this work, inspired by the architectural similarities in standard DNNs and
B-cos networks, we propose 'B-cosification', a novel approach to transform
existing pre-trained models to become inherently interpretable. We perform a
thorough study of design choices to perform this conversion, both for
convolutional neural networks and vision transformers. We find that
B-cosification can yield models that are on par with B-cos models trained from
scratch in terms of interpretability, while often outperforming them in terms
of classification performance at a fraction of the training cost. Subsequently,
we apply B-cosification to a pretrained CLIP model, and show that, even with
limited data and compute cost, we obtain a B-cosified version that is highly
interpretable and competitive on zero shot performance across a variety of
datasets. We release our code and pre-trained model weights at
github.com/shrebox/B-cosification.

Hand action recognition is essential. Communication, human-robot
interactions, and gesture control are dependent on it. Skeleton-based action
recognition traditionally includes hands, which belong to the classes which
remain challenging to correctly recognize to date. We propose a method
specifically designed for hand action recognition which uses relative angular
embeddings and local Spherical Harmonics to create novel hand representations.
The use of Spherical Harmonics creates rotation-invariant representations which
make hand action recognition even more robust against inter-subject differences
and viewpoint changes. We conduct extensive experiments on the hand joints in
the First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose
Annotations, and on the NTU RGB+D 120 dataset, demonstrating the benefit of
using Local Spherical Harmonics Representations. Our code is available at
github.com/KathPra/LSHR_LSHT.

Image restoration networks are usually comprised of an encoder and a decoder,
responsible for aggregating image content from noisy, distorted data and to
restore clean, undistorted images, respectively. Data aggregation as well as
high-resolution image generation both usually come at the risk of involving
aliases, i.e.~standard architectures put their ability to reconstruct the model
input in jeopardy to reach high PSNR values on validation data. The price to be
paid is low model robustness. In this work, we show that simply providing
alias-free paths in state-of-the-art reconstruction transformers supports
improved model robustness at low costs on the restoration performance. We do so
by proposing BOA-Restormer, a transformer-based image restoration model that
executes downsampling and upsampling operations partly in the frequency domain
to ensure alias-free paths along the entire model while potentially preserving
all relevant high-frequency information.

We introduce SLayR, Scene Layout Generation with Rectified flow.
State-of-the-art text-to-image models achieve impressive results. However, they
generate images end-to-end, exposing no fine-grained control over the process.
SLayR presents a novel transformer-based rectified flow model for layout
generation over a token space that can be decoded into bounding boxes and
corresponding labels, which can then be transformed into images using existing
models. We show that established metrics for generated images are inconclusive
for evaluating their underlying scene layout, and introduce a new benchmark
suite, including a carefully designed repeatable human-evaluation procedure
that assesses the plausibility and variety of generated layouts. In contrast to
previous works, which perform well in either high variety or plausibility, we
show that our approach performs well on both of these axes at the same time. It
is also at least 5x times smaller in the number of parameters and 37% faster
than the baselines. Our complete text-to-image pipeline demonstrates the added
benefits of an interpretable and editable intermediate representation.

Low-dimensional embeddings (LDEs) of high-dimensional data are ubiquitous in
science and engineering. They allow us to quickly understand the main
properties of the data, identify outliers and processing errors, and inform the
next steps of data analysis. As such, LDEs have to be faithful to the original
high-dimensional data, i.e., they should represent the relationships that are
encoded in the data, both at a local as well as global scale. The current
generation of LDE approaches focus on reconstructing local distances between
any pair of samples correctly, often out-performing traditional approaches
aiming at all distances. For these approaches, global relationships are,
however, usually strongly distorted, often argued to be an inherent trade-off
between local and global structure learning for embeddings. We suggest a new
perspective on LDE learning, reconstructing angles between data points. We show
that this approach, Mercat, yields good reconstruction across a diverse set of
experiments and metrics, and preserve structures well across all scales.
Compared to existing work, our approach also has a simple formulation,
facilitating future theoretical analysis and algorithmic improvements.

Vision language models (VLMs) have drastically changed the computer vision
model landscape in only a few years, opening an exciting array of new
applications from zero-shot image classification, over to image captioning, and
visual question answering. Unlike pure vision models, they offer an intuitive
way to access visual content through language prompting. The wide applicability
of such models encourages us to ask whether they also align with human vision -
specifically, how far they adopt human-induced visual biases through multimodal
fusion, or whether they simply inherit biases from pure vision models. One
important visual bias is the texture vs. shape bias, or the dominance of local
over global information. In this paper, we study this bias in a wide range of
popular VLMs. Interestingly, we find that VLMs are often more shape-biased than
their vision encoders, indicating that visual biases are modulated to some
extent through text in multimodal models. If text does indeed influence visual
biases, this suggests that we may be able to steer visual biases not just
through visual input but also through language: a hypothesis that we confirm
through extensive experiments. For instance, we are able to steer shape bias
from as low as 49% to as high as 72% through prompting alone. For now, the
strong human bias towards shape (96%) remains out of reach for all tested VLMs.

With the rapid growth of the volume of research fields like computer vision
and computer graphics, researchers require effective and user-friendly
rendering tools to visualize results. While advanced tools like Blender offer
powerful capabilities, they also require a significant effort to master. This
technical report introduces Blendify, a lightweight Python-based framework that
seamlessly integrates with Blender, providing a high-level API for scene
creation and rendering. Blendify reduces the complexity of working with
Blender's native API by automating object creation, handling the colors and
material linking, and implementing features such as shadow-catcher objects
while maintaining support for high-quality ray-tracing rendering output. With a
focus on usability Blendify enables efficient and flexible rendering workflow
for rendering in common computer vision and computer graphics use cases. The
code is available at github.com/ptrvilya/blendify

The estimation of 3D human poses from images has progressed tremendously over
the last few years as measured on standard benchmarks. However, performance in
the open world remains underexplored, as current benchmarks cannot capture its
full extent. Especially in safety-critical systems, it is crucial that 3D pose
estimators are audited before deployment, and their sensitivity towards single
factors or attributes occurring in the operational domain is thoroughly
examined. Nevertheless, we currently lack a benchmark that would enable such
fine-grained analysis. We thus present STAGE, a GenAI data toolkit for auditing
3D human pose estimators. We enable a text-to-image model to control the 3D
human body pose in the generated image. This allows us to create customized
annotated data covering a wide range of open-world attributes. We leverage
STAGE and generate a series of benchmarks to audit the sensitivity of popular
pose estimators towards attributes such as gender, ethnicity, age, clothing,
location, and weather. Our results show that the presence of such naturally
occurring attributes can cause severe degradation in the performance of pose
estimators and leads us to question if they are ready for open-world
deployment.

Deep learning is increasingly relevant in our daily lives, as it simplifies tedious tasks and enhances quality of life across various domains such as entertainment, learning, automatic assistance, and autonomous driving. However, the demand for more data to train models for emerging tasks is increasing dramatically. Deep learning models heavily depend on the quality and quantity of data, necessitating high-quality labeled datasets. Yet, each task requires different types of annotations for training and evaluation, posing challenges in obtaining comprehensive supervision. The acquisition of annotations is not only resource-intensive in terms of time and cost but also introduces biases, such as granularity in classification, where distinctions like specific breeds versus generic categories may arise. Furthermore, the dynamic nature of the world causes the challenge that previously annotated data becomes potentially irrelevant, and new categories and rare occurrences continually emerge, making it impossible to label every aspect of the world.
Therefore, this thesis aims to explore various supervision scenarios to mitigate the need for full supervision and reduce data acquisition costs. Specifically, we investigate learning without labels, referred to as self-supervised and unsupervised methods, to better understand video and image representations. To learn from data without labels, we leverage injected priors such as motion speed, direction, action order in videos, or semantic information granularity to obtain powerful data representations. Further, we study scenarios involving reduced supervision levels. To reduce annotation costs, first, we propose to omit precise annotations for one modality in multimodal learning, namely in text-video and image-video settings, and transfer available knowledge to large copora of video data. Second, we study semi-supervised learning scenarios, where only a subset of annotated data alongside unlabeled data is available, and propose to revisit regularization constraints and improve generalization to unlabeled data. Additionally, we address scenarios where parts of available data is inherently limited due to privacy and security reasons or naturally rare events, which not only restrict annotations but also limit the overall data volume. For these scenarios, we propose methods that carefully balance between previously obtained knowledge and incoming limited data by introducing a calibration method or combining a space reservation technique with orthogonality constraints. Finally, we explore multimodal and unimodal open-world scenarios where the model is asked to generalize beyond the given set of object or action classes. Specifically, we propose a new challenging setting on multimodal egocentric videos and propose an adaptation method for vision-language models to generalize on egocentric domain. Moreover, we study unimodal image recognition in an open-set setting and propose to disentangle open-set detection and image classification tasks that effectively improve generalization in different settings.
In summary, this thesis investigates challenges arising when full supervision for training models is not available. We develop methods to understand learning dynamics and the role of biases in data, while also proposing novel setups to advance training with less supervision.

While being very successful in solving many downstream tasks, the application
of deep neural networks is limited in real-life scenarios because of their
susceptibility to domain shifts such as common corruptions, and adversarial
attacks. The existence of adversarial examples and data corruption
significantly reduces the performance of deep classification models.
Researchers have made strides in developing robust neural architectures to
bolster decisions of deep classifiers. However, most of these works rely on
effective adversarial training methods, and predominantly focus on overall
model robustness, disregarding class-wise differences in robustness, which are
critical. Exploiting weakly robust classes is a potential avenue for attackers
to fool the image recognition models. Therefore, this study investigates
class-to-class biases across adversarially trained robust classification models
to understand their latent space structures and analyze their strong and weak
class-wise properties. We further assess the robustness of classes against
common corruptions and adversarial attacks, recognizing that class
vulnerability extends beyond the number of correct classifications for a
specific class. We find that the number of false positives of classes as
specific target classes significantly impacts their vulnerability to attacks.
Through our analysis on the Class False Positive Score, we assess a fair
evaluation of how susceptible each class is to misclassification.

We introduce FaceGPT, a self-supervised learning framework for Large
Vision-Language Models (VLMs) to reason about 3D human faces from images and
text. Typical 3D face reconstruction methods are specialized algorithms that
lack semantic reasoning capabilities. FaceGPT overcomes this limitation by
embedding the parameters of a 3D morphable face model (3DMM) into the token
space of a VLM, enabling the generation of 3D faces from both textual and
visual inputs. FaceGPT is trained in a self-supervised manner as a model-based
autoencoder from in-the-wild images. In particular, the hidden state of LLM is
projected into 3DMM parameters and subsequently rendered as 2D face image to
guide the self-supervised learning process via image-based reconstruction.
Without relying on expensive 3D annotations of human faces, FaceGPT obtains a
detailed understanding about 3D human faces, while preserving the capacity to
understand general user instructions. Our experiments demonstrate that FaceGPT
not only achieves high-quality 3D face reconstructions but also retains the
ability for general-purpose visual instruction following. Furthermore, FaceGPT
learns fully self-supervised to generate 3D faces based on complex textual
inputs, which opens a new direction in human face analysis.

Synthesizing natural human motion that adapts to complex environments while
allowing creative control remains a fundamental challenge in motion synthesis.
Existing models often fall short, either by assuming flat terrain or lacking
the ability to control motion semantics through text. To address these
limitations, we introduce SCENIC, a diffusion model designed to generate human
motion that adapts to dynamic terrains within virtual scenes while enabling
semantic control through natural language. The key technical challenge lies in
simultaneously reasoning about complex scene geometry while maintaining text
control. This requires understanding both high-level navigation goals and
fine-grained environmental constraints. The model must ensure physical
plausibility and precise navigation across varied terrain, while also
preserving user-specified text control, such as ``carefully stepping over
obstacles" or ``walking upstairs like a zombie." Our solution introduces a
hierarchical scene reasoning approach. At its core is a novel scene-dependent,
goal-centric canonicalization that handles high-level goal constraint, and is
complemented by an ego-centric distance field that captures local geometric
details. This dual representation enables our model to generate physically
plausible motion across diverse 3D scenes. By implementing frame-wise text
alignment, our system achieves seamless transitions between different motion
styles while maintaining scene constraints. Experiments demonstrate our novel
diffusion model generates arbitrarily long human motions that both adapt to
complex scenes with varying terrain surfaces and respond to textual prompts.
Additionally, we show SCENIC can generalize to four real-scene datasets. Our
code, dataset, and models will be released at
\url{https://virtualhumans.mpi-inf.mpg.de/scenic/}.

Deep learning models have proven to be successful in a wide
range of machine learning tasks. Yet, they are often highly sensitive to
perturbations on the input data which can lead to incorrect decisions
with high confidence, hampering their deployment for practical
use-cases. Thus, finding architectures that are (more) robust against
perturbations has received much attention in recent years. Just like the
search for well-performing architectures in terms of clean accuracy,
this usually involves a tedious trial-and-error process with one
additional challenge: the evaluation of a network's robustness is
significantly more expensive than its evaluation for clean accuracy.
Thus, the aim of this paper is to facilitate better streamlined research
on architectural design choices with respect to their impact on
robustness as well as, for example, the evaluation of surrogate measures
for robustness. We therefore borrow one of the most commonly considered
search spaces for neural architecture search for image classification,
NAS-Bench-201, which contains a manageable size of 6466 non-isomorphic
network designs. We evaluate all these networks on a range of common
adversarial attacks and corruption types and introduce a database on
neural architecture design and robustness evaluations. We further
present three exemplary use cases of this dataset, in which we (i)
benchmark robustness measurements based on Jacobian and Hessian matrices
for their robustness predictability, (ii) perform neural architecture
search on robust accuracies, and (iii) provide an initial analysis of
how architectural design choices affect robustness. We find that
carefully crafting the topology of a network can have substantial impact
on its robustness, where networks with the same parameter count range in
mean adversarial robust accuracy from 20%-41%.

Local coherence is essential for long-form text generation models. We identify two important aspects of local coherence within the visual storytelling task: (1) the model needs to represent re-occurrences of characters within the image sequence in order to mention them correctly in the story; (2) character representations should enable us to find instances of the same characters and distinguish different characters. In this paper, we propose a loss function inspired by a linguistic theory of coherence for self-supervised learning for image sequence representations. We further propose combining features from an object and a face detector to construct stronger character features. To evaluate input-output relevance that current reference-based metrics don't measure, we propose a character matching metric to check whether the models generate referring expressions correctly for characters in input image sequences. Experiments on a visual story generation dataset show that our proposed features and loss function are effective for generating more coherent and visually grounded stories.

Human performance capture is a highly important computer vision problem with
many applications in movie production and virtual/augmented reality. Many
previous performance capture approaches either required expensive multi-view
setups or did not recover dense space-time coherent geometry with
frame-to-frame correspondences. We propose a novel deep learning approach for
monocular dense human performance capture. Our method is trained in a weakly
supervised manner based on multi-view supervision completely removing the need
for training data with 3D ground truth annotations. The network architecture is
based on two separate networks that disentangle the task into a pose estimation
and a non-rigid surface deformation step. Extensive qualitative and
quantitative evaluations show that our approach outperforms the state of the
art in terms of quality and robustness. This work is an extended version of
DeepCap where we provide more detailed explanations, comparisons and results as
well as applications.

Minimum cost lifted multicut problem is a generalization of the multicut problem and is a means to optimizing a decomposition of a graph w.r.t. both positive and negative edge costs. Its main advantage is that multicut-based formulations do not require the number of components given a priori; instead, it is deduced from the solution. However, the standard multicut cost function is limited to pairwise relationships between nodes, while several important applications either require or can benefit from a higher-order cost function, i.e. hyper-edges. In this paper, we propose a pseudo-boolean formulation for a multiple model fitting problem. It is based on a formulation of any-order minimum cost lifted multicuts, which allows to partition an undirected graph with pairwise connectivity such as to minimize costs defined over any set of hyper-edges. As the proposed formulation is NP-hard and the branch-and-bound algorithm is too slow in practice, we propose an efficient local search algorithm for inference into resulting problems. We demonstrate versatility and effectiveness of our approach in several applications: geometric multiple model fitting, homography and motion estimation, motion segmentation.

Transformers increasingly dominate the machine learning landscape across many
tasks and domains, which increases the importance for understanding their
outputs. While their attention modules provide partial insight into their inner
workings, the attention scores have been shown to be insufficient for
explaining the models as a whole. To address this, we propose B-cos
transformers, which inherently provide holistic explanations for their
decisions. Specifically, we formulate each model component - such as the
multi-layer perceptrons, attention layers, and the tokenisation module - to be
dynamic linear, which allows us to faithfully summarise the entire transformer
via a single linear transform. We apply our proposed design to Vision
Transformers (ViTs) and show that the resulting models, dubbed Bcos-ViTs, are
highly interpretable and perform competitively to baseline ViTs on ImageNet.
Code will be made available soon.

Much of the world's most valued data is stored in relational databases and
data warehouses, where the data is organized into many tables connected by
primary-foreign key relations. However, building machine learning models using
this data is both challenging and time consuming. The core problem is that no
machine learning method is capable of learning on multiple tables
interconnected by primary-foreign key relations. Current methods can only learn
from a single table, so the data must first be manually joined and aggregated
into a single training table, the process known as feature engineering. Feature
engineering is slow, error prone and leads to suboptimal models. Here we
introduce an end-to-end deep representation learning approach to directly learn
on data laid out across multiple tables. We name our approach Relational Deep
Learning (RDL). The core idea is to view relational databases as a temporal,
heterogeneous graph, with a node for each row in each table, and edges
specified by primary-foreign key links. Message Passing Graph Neural Networks
can then automatically learn across the graph to extract representations that
leverage all input data, without any manual feature engineering. Relational
Deep Learning leads to more accurate models that can be built much faster. To
facilitate research in this area, we develop RelBench, a set of benchmark
datasets and an implementation of Relational Deep Learning. The data covers a
wide spectrum, from discussions on Stack Exchange to book reviews on the Amazon
Product Catalog. Overall, we define a new research area that generalizes graph
machine learning and broadens its applicability to a wide set of AI use cases.

Convolutional neural networks encode images through a sequence of
convolutions, normalizations and non-linearities as well as downsampling
operations into potentially strong semantic embeddings. Yet, previous work
showed that even slight mistakes during sampling, leading to aliasing, can be
directly attributed to the networks' lack in robustness. To address such issues
and facilitate simpler and faster adversarial training, [12] recently proposed
FLC pooling, a method for provably alias-free downsampling - in theory. In this
work, we conduct a further analysis through the lens of signal processing and
find that such current pooling methods, which address aliasing in the frequency
domain, are still prone to spectral leakage artifacts. Hence, we propose
aliasing and spectral artifact-free pooling, short ASAP. While only introducing
a few modifications to FLC pooling, networks using ASAP as downsampling method
exhibit higher native robustness against common corruptions, a property that
FLC pooling was missing. ASAP also increases native robustness against
adversarial attacks on high and low resolution data while maintaining similar
clean accuracy or even outperforming the baseline.

Zero-shot semantic segmentation (ZS3) aims to segment the novel categories
that have not been seen in the training. Existing works formulate ZS3 as a
pixel-level zero-shot classification problem, and transfer semantic knowledge
from seen classes to unseen ones with the help of language models pre-trained
only with texts. While simple, the pixel-level ZS3 formulation shows the
limited capability to integrate vision-language models that are often
pre-trained with image-text pairs and currently demonstrate great potential for
vision tasks. Inspired by the observation that humans often perform
segment-level semantic labeling, we propose to decouple the ZS3 into two
sub-tasks: 1) a class-agnostic grouping task to group the pixels into segments.
2) a zero-shot classification task on segments. The former sub-task does not
involve category information and can be directly transferred to group pixels
for unseen classes. The latter subtask performs at segment-level and provides a
natural way to leverage large-scale vision-language models pre-trained with
image-text pairs (e.g. CLIP) for ZS3. Based on the decoupling formulation, we
propose a simple and effective zero-shot semantic segmentation model, called
ZegFormer, which outperforms the previous methods on ZS3 standard benchmarks by
large margins, e.g., 35 points on the PASCAL VOC and 3 points on the COCO-Stuff
in terms of mIoU for unseen classes. Code will be released at
github.com/dingjiansw101/ZegFormer.

In this paper, we propose a novel co-learning framework (CoSSL) with
decoupled representation learning and classifier learning for imbalanced SSL.
To handle the data imbalance, we devise Tail-class Feature Enhancement (TFE)
for classifier learning. Furthermore, the current evaluation protocol for
imbalanced SSL focuses only on balanced test sets, which has limited
practicality in real-world scenarios. Therefore, we further conduct a
comprehensive evaluation under various shifted test distributions. In
experiments, we show that our approach outperforms other methods over a large
range of shifted distributions, achieving state-of-the-art performance on
benchmark datasets ranging from CIFAR-10, CIFAR-100, ImageNet, to Food-101. Our
code will be made publicly available.

As acquiring pixel-wise annotations of real-world images for semantic
segmentation is a costly process, a model can instead be trained with more
accessible synthetic data and adapted to real images without requiring their
annotations. This process is studied in unsupervised domain adaptation (UDA).
Even though a large number of methods propose new adaptation strategies, they
are mostly based on outdated network architectures. As the influence of recent
network architectures has not been systematically studied, we first benchmark
different network architectures for UDA and then propose a novel UDA method,
DAFormer, based on the benchmark results. The DAFormer network consists of a
Transformer encoder and a multi-level context-aware feature fusion decoder. It
is enabled by three simple but crucial training strategies to stabilize the
training and to avoid overfitting DAFormer to the source domain: While the Rare
Class Sampling on the source domain improves the quality of pseudo-labels by
mitigating the confirmation bias of self-training towards common classes, the
Thing-Class ImageNet Feature Distance and a learning rate warmup promote
feature transfer from ImageNet pretraining. DAFormer significantly improves the
state-of-the-art performance by 10.8 mIoU for GTA->Cityscapes and 5.4 mIoU for
Synthia->Cityscapes and enables learning even difficult classes such as train,
bus, and truck well. The implementation is available at
github.com/lhoyer/DAFormer.

Although considerable progress has been made in semantic scene understanding
under clear weather, it is still a tough problem under adverse weather
conditions, such as dense fog, due to the uncertainty caused by imperfect
observations. Besides, difficulties in collecting and labeling foggy images
hinder the progress of this field. Considering the success in semantic scene
understanding under clear weather, we think it is reasonable to transfer
knowledge learned from clear images to the foggy domain. As such, the problem
becomes to bridge the domain gap between clear images and foggy images. Unlike
previous methods that mainly focus on closing the domain gap caused by fog --
defogging the foggy images or fogging the clear images, we propose to alleviate
the domain gap by considering fog influence and style variation simultaneously.
The motivation is based on our finding that the style-related gap and the
fog-related gap can be divided and closed respectively, by adding an
intermediate domain. Thus, we propose a new pipeline to cumulatively adapt
style, fog and the dual-factor (style and fog). Specifically, we devise a
unified framework to disentangle the style factor and the fog factor
separately, and then the dual-factor from images in different domains.
Furthermore, we collaborate the disentanglement of three factors with a novel
cumulative loss to thoroughly disentangle these three factors. Our method
achieves the state-of-the-art performance on three benchmarks and shows
generalization ability in rainy and snowy scenes.

Multi-Camera Multi-Object Tracking is currently drawing attention in the
computer vision field due to its superior performance in real-world
applications such as video surveillance with crowded scenes or in vast space.
In this work, we propose a mathematically elegant multi-camera multiple object
tracking approach based on a spatial-temporal lifted multicut formulation. Our
model utilizes state-of-the-art tracklets produced by single-camera trackers as
proposals. As these tracklets may contain ID-Switch errors, we refine them
through a novel pre-clustering obtained from 3D geometry projections. As a
result, we derive a better tracking graph without ID switches and more precise
affinity costs for the data association phase. Tracklets are then matched to
multi-camera trajectories by solving a global lifted multicut formulation that
incorporates short and long-range temporal interactions on tracklets located in
the same camera as well as inter-camera ones. Experimental results on the
WildTrack dataset yield near-perfect result, outperforming state-of-the-art
trackers on Campus while being on par on the PETS-09 dataset. We will make our
implementations available upon acceptance of the paper.

3D human motion capture from monocular RGB images respecting interactions of
a subject with complex and possibly deformable environments is a very
challenging, ill-posed and under-explored problem. Existing methods address it
only weakly and do not model possible surface deformations often occurring when
humans interact with scene surfaces. In contrast, this paper proposes
MoCapDeform, i.e., a new framework for monocular 3D human motion capture that
is the first to explicitly model non-rigid deformations of a 3D scene for
improved 3D human pose estimation and deformable environment reconstruction.
MoCapDeform accepts a monocular RGB video and a 3D scene mesh aligned in the
camera space. It first localises a subject in the input monocular video along
with dense contact labels using a new raycasting based strategy. Next, our
human-environment interaction constraints are leveraged to jointly optimise
global 3D human poses and non-rigid surface deformations. MoCapDeform achieves
superior accuracy than competing methods on several datasets, including our
newly recorded one with deforming background scenes.

The minimum cost multicut problem is the NP-hard/APX-hard combinatorial
optimization problem of partitioning a real-valued edge-weighted graph such as
to minimize the total cost of the partition. While graph convolutional neural
networks (GNN) have proven to be promising in the context of combinatorial
optimization, most of them are only tailored to or tested on positive-valued
edge weights, i.e. they do not comply to the nature of the multicut problem. We
therefore adapt various GNN architectures including Graph Convolutional
Networks, Signed Graph Convolutional Networks and Graph Isomorphic Networks to
facilitate the efficient encoding of real-valued edge costs. Moreover, we
employ a reformulation of the multicut ILP constraints to a polynomial program
as loss function that allows to learn feasible multicut solutions in a scalable
way. Thus, we provide the first approach towards end-to-end trainable
multicuts. Our findings support that GNN approaches can produce good solutions
in practice while providing lower computation times and largely improved
scalability compared to LP solvers and optimized heuristics, especially when
considering large instances.

The Minimum Cost Multicut Problem (MP) is a popular way for obtaining a graph
decomposition by optimizing binary edge labels over edge costs. While the
formulation of a MP from independently estimated costs per edge is highly
flexible and intuitive, solving the MP is NP-hard and time-expensive. As a
remedy, recent work proposed to predict edge probabilities with awareness to
potential conflicts by incorporating cycle constraints in the prediction
process. We argue that such formulation, while providing a first step towards
end-to-end learnable edge weights, is suboptimal, since it is built upon a
loose relaxation of the MP. We therefore propose an adaptive CRF that allows to
progressively consider more violated constraints and, in consequence, to issue
solutions with higher validity. Experiments on the BSDS500 benchmark for
natural image segmentation as well as on electron microscopic recordings show
that our approach yields more precise edge detection and image segmentation.

Planning an optimal route in a complex environment requires efficient
reasoning about the surrounding scene. While human drivers prioritize important
objects and ignore details not relevant to the decision, learning-based
planners typically extract features from dense, high-dimensional grid
representations containing all vehicle and road context information. In this
paper, we propose PlanT, a novel approach for planning in the context of
self-driving that uses a standard transformer architecture. PlanT is based on
imitation learning with a compact object-level input representation. On the
Longest6 benchmark for CARLA, PlanT outperforms all prior methods (matching the
driving score of the expert) while being 5.3x faster than equivalent
pixel-based planning baselines during inference. Combining PlanT with an
off-the-shelf perception module provides a sensor-based driving system that is
more than 10 points better in terms of driving score than the existing state of
the art. Furthermore, we propose an evaluation protocol to quantify the ability
of planners to identify relevant objects, providing insights regarding their
decision-making. Our results indicate that PlanT can focus on the most relevant
object in the scene, even when this object is geometrically distant.

Semi-supervised learning (SSL) has shown great promise in leveraging
unlabeled data to improve model performance. While standard SSL assumes uniform
data distribution, we consider a more realistic and challenging setting called
imbalanced SSL, where imbalanced class distributions occur in both labeled and
unlabeled data. Although there are existing endeavors to tackle this challenge,
their performance degenerates when facing severe imbalance since they can not
reduce the class imbalance sufficiently and effectively. In this paper, we
study a simple yet overlooked baseline -- SimiS -- which tackles data imbalance
by simply supplementing labeled data with pseudo-labels, according to the
difference in class distribution from the most frequent class. Such a simple
baseline turns out to be highly effective in reducing class imbalance. It
outperforms existing methods by a significant margin, e.g., 12.8%, 13.6%, and
16.7% over previous SOTA on CIFAR100-LT, FOOD101-LT, and ImageNet127
respectively. The reduced imbalance results in faster convergence and better
pseudo-label accuracy of SimiS. The simplicity of our method also makes it
possible to be combined with other re-balancing techniques to improve the
performance further. Moreover, our method shows great robustness to a wide
range of data distributions, which holds enormous potential in practice. Code
will be publicly available.

Unintentional actions are rare occurrences that are difficult to define
precisely and that are highly dependent on the temporal context of the action.
In this work, we explore such actions and seek to identify the points in videos
where the actions transition from intentional to unintentional. We propose a
multi-stage framework that exploits inherent biases such as motion speed,
motion direction, and order to recognize unintentional actions. To enhance
representations via self-supervised training for the task of unintentional
action recognition we propose temporal transformations, called Temporal
Transformations of Inherent Biases of Unintentional Actions (T2IBUA). The
multi-stage approach models the temporal information on both the level of
individual frames and full clips. These enhanced representations show strong
performance for unintentional action recognition tasks. We provide an extensive
ablation study of our framework and report results that significantly improve
over the state-of-the-art.

Improving model's generalizability against domain shifts is crucial,
especially for safety-critical applications such as autonomous driving.
Real-world domain styles can vary substantially due to environment changes and
sensor noises, but deep models only know the training domain style. Such domain
style gap impedes model generalization on diverse real-world domains. Our
proposed Normalization Perturbation (NP) can effectively overcome this domain
style overfitting problem. We observe that this problem is mainly caused by the
biased distribution of low-level features learned in shallow CNN layers. Thus,
we propose to perturb the channel statistics of source domain features to
synthesize various latent styles, so that the trained deep model can perceive
diverse potential domains and generalizes well even without observations of
target domain data in training. We further explore the style-sensitive channels
for effective style synthesis. Normalization Perturbation only relies on a
single source domain and is surprisingly effective and extremely easy to
implement. Extensive experiments verify the effectiveness of our method for
generalizing models under real-world domain shifts.

In everyday lives, humans naturally modify the surrounding environment
through interactions, e.g., moving a chair to sit on it. To reproduce such
interactions in virtual spaces (e.g., metaverse), we need to be able to capture
and model them, including changes in the scene geometry, ideally from
ego-centric input alone (head camera and body-worn inertial sensors). This is
an extremely hard problem, especially since the object/scene might not be
visible from the head camera (e.g., a human not looking at a chair while
sitting down, or not looking at the door handle while opening a door). In this
paper, we present HOPS, the first method to capture interactions such as
dragging objects and opening doors from ego-centric data alone. Central to our
method is reasoning about human-object interactions, allowing to track objects
even when they are not visible from the head camera. HOPS localizes and
registers both the human and the dynamic object in a pre-scanned static scene.
HOPS is an important first step towards advanced AR/VR applications based on
immersive virtual universes, and can provide human-centric training data to
teach machines to interact with their surroundings. The supplementary video,
data, and code will be available on our project page at
virtualhumans.mpi-inf.mpg.de/hops/

This paper introduces DGNet, a novel deep framework that exploits object
gradient supervision for camouflaged object detection (COD). It decouples the
task into two connected branches, i.e., a context and a texture encoder. The
essential connection is the gradient-induced transition, representing a soft
grouping between context and texture features. Benefiting from the simple but
efficient framework, DGNet outperforms existing state-of-the-art COD models by
a large margin. Notably, our efficient version, DGNet-S, runs in real-time (80
fps) and achieves comparable results to the cutting-edge model
JCSOD-CVPR$_{21}$ with only 6.82% parameters. Application results also show
that the proposed DGNet performs well in polyp segmentation, defect detection,
and transparent object segmentation tasks. Codes will be made available at
github.com/GewelsJI/DGNet.

In this report, we present the 1st place solution for motion prediction track
in 2022 Waymo Open Dataset Challenges. We propose a novel Motion Transformer
framework for multimodal motion prediction, which introduces a small set of
novel motion query pairs for generating better multimodal future trajectories
by jointly performing the intention localization and iterative motion
refinement. A simple model ensemble strategy with non-maximum-suppression is
adopted to further boost the final performance. Our approach achieves the 1st
place on the motion prediction leaderboard of 2022 Waymo Open Dataset
Challenges, outperforming other methods with remarkable margins. Code will be
available at github.com/sshaoshuai/MTR.

Deep learning is becoming increasingly relevant for many high-stakes applications such as autonomous driving or medical diagnosis where wrong decisions can have massive impact on human lives. Unfortunately, deep neural networks are typically assessed solely based on generalization, e.g., accuracy on a fixed test set. However, this is clearly insufficient for safe deployment as potential malicious actors and distribution shifts or the effects of quantization and unreliable hardware are disregarded. Thus, recent work additionally evaluates performance on potentially manipulated or corrupted inputs as well as after quantization and deployment on specialized hardware. In such settings, it is also important to obtain reasonable estimates of the model's confidence alongside its predictions. This thesis studies robustness and uncertainty estimation in deep learning along three main directions: First, we consider so-called adversarial examples, slightly perturbed inputs causing severe drops in accuracy. Second, we study weight perturbations, focusing particularly on bit errors in quantized weights. This is relevant for deploying models on special-purpose hardware for efficient inference, so-called accelerators. Finally, we address uncertainty estimation to improve robustness and provide meaningful statistical performance guarantees for safe deployment. In detail, we study the existence of adversarial examples with respect to the underlying data manifold. In this context, we also investigate adversarial training which improves robustness by augmenting training with adversarial examples at the cost of reduced accuracy. We show that regular adversarial examples leave the data manifold in an almost orthogonal direction. While we find no inherent trade-off between robustness and accuracy, this contributes to a higher sample complexity as well as severe overfitting of adversarial training. Using a novel measure of flatness in the robust loss landscape with respect to weight changes, we also show that robust overfitting is caused by converging to particularly sharp minima. In fact, we find a clear correlation between flatness and good robust generalization. Further, we study random and adversarial bit errors in quantized weights. In accelerators, random bit errors occur in the memory when reducing voltage with the goal of improving energy-efficiency. Here, we consider a robust quantization scheme, use weight clipping as regularization and perform random bit error training to improve bit error robustness, allowing considerable energy savings without requiring hardware changes. In contrast, adversarial bit errors are maliciously introduced through hardware- or software-based attacks on the memory, with severe consequences on performance. We propose a novel adversarial bit error attack to study this threat and use adversarial bit error training to improve robustness and thereby also the accelerator's security. Finally, we view robustness in the context of uncertainty estimation. By encouraging low-confidence predictions on adversarial examples, our confidence-calibrated adversarial training successfully rejects adversarial, corrupted as well as out-of-distribution examples at test time. Thereby, we are also able to improve the robustness-accuracy trade-off compared to regular adversarial training. However, even robust models do not provide any guarantee for safe deployment. To address this problem, conformal prediction allows the model to predict confidence sets with user-specified guarantee of including the true label. Unfortunately, as conformal prediction is usually applied after training, the model is trained without taking this calibration step into account. To address this limitation, we propose conformal training which allows training conformal predictors end-to-end with the underlying model. This not only improves the obtained uncertainty estimates but also enables optimizing application-specific objectives without losing the provided guarantee. Besides our work on robustness or uncertainty, we also address the problem of 3D shape completion of partially observed point clouds. Specifically, we consider an autonomous driving or robotics setting where vehicles are commonly equipped with LiDAR or depth sensors and obtaining a complete 3D representation of the environment is crucial. However, ground truth shapes that are essential for applying deep learning techniques are extremely difficult to obtain. Thus, we propose a weakly-supervised approach that can be trained on the incomplete point clouds while offering efficient inference. In summary, this thesis contributes to our understanding of robustness against both input and weight perturbations. To this end, we also develop methods to improve robustness alongside uncertainty estimation for safe deployment of deep learning methods in high-stakes applications. In the particular context of autonomous driving, we also address 3D shape completion of sparse point clouds.

Structured prediction problems are one of the fundamental tools in machine
learning. In order to facilitate algorithm development for their numerical
solution, we collect in one place a large number of datasets in easy to read
formats for a diverse set of problem classes. We provide archival links to
datasets, description of the considered problems and problem formats, and a
short summary of problem characteristics including size, number of instances
etc. For reference we also give a non-exhaustive selection of algorithms
proposed in the literature for their solution. We hope that this central
repository will make benchmarking and comparison to established works easier.
We welcome submission of interesting new datasets and algorithms for inclusion
in our archive.

Modern deep learning architecture utilize batch normalization (BN) to
stabilize training and improve accuracy. It has been shown that the BN layers
alone are surprisingly expressive. In the context of robustness against
adversarial examples, however, BN is argued to increase vulnerability. That is,
BN helps to learn fragile features. Nevertheless, BN is still used in
adversarial training, which is the de-facto standard to learn robust features.
In order to shed light on the role of BN in adversarial training, we
investigate to what extent the expressiveness of BN can be used to robustify
fragile features in comparison to random features. On CIFAR10, we find that
adversarially fine-tuning just the BN layers can result in non-trivial
adversarial robustness. Adversarially training only the BN layers from scratch,
in contrast, is not able to convey meaningful adversarial robustness. Our
results indicate that fragile features can be used to learn models with
moderate adversarial robustness, while random features cannot

Current efficient LiDAR-based detection frameworks are lacking in exploiting
object relations, which naturally present in both spatial and temporal manners.
To this end, we introduce a simple, efficient, and effective two-stage
detector, termed as Ret3D. At the core of Ret3D is the utilization of novel
intra-frame and inter-frame relation modules to capture the spatial and
temporal relations accordingly. More Specifically, intra-frame relation module
(IntraRM) encapsulates the intra-frame objects into a sparse graph and thus
allows us to refine the object features through efficient message passing. On
the other hand, inter-frame relation module (InterRM) densely connects each
object in its corresponding tracked sequences dynamically, and leverages such
temporal information to further enhance its representations efficiently through
a lightweight transformer network. We instantiate our novel designs of IntraRM
and InterRM with general center-based or anchor-based detectors and evaluate
them on Waymo Open Dataset (WOD). With negligible extra overhead, Ret3D
achieves the state-of-the-art performance, being 5.5% and 3.2% higher than the
recent competitor in terms of the LEVEL 1 and LEVEL 2 mAPH metrics on vehicle
detection, respectively.

We present TOCH, a method for refining incorrect 3D hand-object interaction
sequences using a data prior. Existing hand trackers, especially those that
rely on very few cameras, often produce visually unrealistic results with
hand-object intersection or missing contacts. Although correcting such errors
requires reasoning about temporal aspects of interaction, most previous work
focus on static grasps and contacts. The core of our method are TOCH fields, a
novel spatio-temporal representation for modeling correspondences between hands
and objects during interaction. The key component is a point-wise
object-centric representation which encodes the hand position relative to the
object. Leveraging this novel representation, we learn a latent manifold of
plausible TOCH fields with a temporal denoising auto-encoder. Experiments
demonstrate that TOCH outperforms state-of-the-art (SOTA) 3D hand-object
interaction models, which are limited to static grasps and contacts. More
importantly, our method produces smooth interactions even before and after
contact. Using a single trained TOCH model, we quantitatively and qualitatively
demonstrate its usefulness for 1) correcting erroneous reconstruction results
from off-the-shelf RGB/RGB-D hand-object reconstruction methods, 2) de-noising,
and 3) grasp transfer across objects. We will release our code and trained
model on our project page at virtualhumans.mpi-inf.mpg.de/toch/

Skeleton-based action recognition aims to predict human actions given human
joint coordinates with skeletal interconnections. To model such off-grid data
points and their co-occurrences, Transformer-based formulations would be a
natural choice. However, Transformers still lag behind state-of-the-art methods
using graph convolutional networks (GCNs). Transformers assume that the input
is permutation-invariant and homogeneous (partially alleviated by positional
encoding), which ignores an important characteristic of skeleton data, i.e.,
bone connectivity. Furthermore, each type of body joint has a clear physical
meaning in human motion, i.e., motion retains an intrinsic relationship
regardless of the joint coordinates, which is not explored in Transformers. In
fact, certain re-occurring groups of body joints are often involved in specific
actions, such as the subconscious hand movement for keeping balance. Vanilla
attention is incapable of describing such underlying relations that are
persistent and beyond pair-wise. In this work, we aim to exploit these unique
aspects of skeleton data to close the performance gap between Transformers and
GCNs. Specifically, we propose a new self-attention (SA) extension, named
Hypergraph Self-Attention (HyperSA), to incorporate inherently higher-order
relations into the model. The K-hop relative positional embeddings are also
employed to take bone connectivity into account. We name the resulting model
Hyperformer, and it achieves comparable or better performance w.r.t. accuracy
and efficiency than state-of-the-art GCN architectures on NTU RGB+D, NTU RGB+D
120, and Northwestern-UCLA datasets. On the largest NTU RGB+D 120 dataset, the
significantly improved performance reached by our Hyperformer demonstrates the
underestimated potential of Transformer models in this field.