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

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

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.

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

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

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

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

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

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

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

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

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

Besides standard cameras, autonomous vehicles typically include multiple<br>additional sensors, such as lidars and radars, which help acquire richer<br>information for perceiving the content of the driving scene. While several<br>recent works focus on fusing certain pairs of sensors - such as camera and<br>lidar or camera and radar - by using architectural components specific to the<br>examined setting, a generic and modular sensor fusion architecture is missing<br>from the literature. In this work, we focus on 2D object detection, a<br>fundamental high-level task which is defined on the 2D image domain, and<br>propose HRFuser, a multi-resolution sensor fusion architecture that scales<br>straightforwardly to an arbitrary number of input modalities. The design of<br>HRFuser is based on state-of-the-art high-resolution networks for image-only<br>dense prediction and incorporates a novel multi-window cross-attention block as<br>the means to perform fusion of multiple modalities at multiple resolutions.<br>Even though cameras alone provide very informative features for 2D detection,<br>we demonstrate via extensive experiments on the nuScenes and Seeing Through Fog<br>datasets that our model effectively leverages complementary features from<br>additional modalities, substantially improving upon camera-only performance and<br>consistently outperforming state-of-the-art fusion methods for 2D detection<br>both in normal and adverse conditions. The source code will be made publicly<br>available.<br>

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

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

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

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

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

Prior work has extensively studied the latent space structure of GANs for<br>unconditional image synthesis, enabling global editing of generated images by<br>the unsupervised discovery of interpretable latent directions. However, the<br>discovery of latent directions for conditional GANs for semantic image<br>synthesis (SIS) has remained unexplored. In this work, we specifically focus on<br>addressing this gap. We propose a novel optimization method for finding<br>spatially disentangled class-specific directions in the latent space of<br>pretrained SIS models. We show that the latent directions found by our method<br>can effectively control the local appearance of semantic classes, e.g.,<br>changing their internal structure, texture or color independently from each<br>other. Visual inspection and quantitative evaluation of the discovered GAN<br>controls on various datasets demonstrate that our method discovers a diverse<br>set of unique and semantically meaningful latent directions for class-specific<br>edits.<br>

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

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

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

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

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

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

Deep neural network (DNN) accelerators received considerable attention in<br>past years due to saved energy compared to mainstream hardware. Low-voltage<br>operation of DNN accelerators allows to further reduce energy consumption<br>significantly, however, causes bit-level failures in the memory storing the<br>quantized DNN weights. In this paper, we show that a combination of robust<br>fixed-point quantization, weight clipping, and random bit error training<br>(RandBET) improves robustness against random bit errors in (quantized) DNN<br>weights significantly. This leads to high energy savings from both low-voltage<br>operation as well as low-precision quantization. Our approach generalizes<br>across operating voltages and accelerators, as demonstrated on bit errors from<br>profiled SRAM arrays. We also discuss why weight clipping alone is already a<br>quite effective way to achieve robustness against bit errors. Moreover, we<br>specifically discuss the involved trade-offs regarding accuracy, robustness and<br>precision: Without losing more than 1% in accuracy compared to a normally<br>trained 8-bit DNN, we can reduce energy consumption on CIFAR-10 by 20%. Higher<br>energy savings of, e.g., 30%, are possible at the cost of 2.5% accuracy, even<br>for 4-bit DNNs.<br>

We present a massively parallel Lagrange decomposition method for solving 0-1<br>integer linear programs occurring in structured prediction. We propose a new<br>iterative update scheme for solving the Lagrangean dual and a perturbation<br>technique for decoding primal solutions. For representing subproblems we follow<br>Lange et al. (2021) and use binary decision diagrams (BDDs). Our primal and<br>dual algorithms require little synchronization between subproblems and<br>optimization over BDDs needs only elementary operations without complicated<br>control flow. This allows us to exploit the parallelism offered by GPUs for all<br>components of our method. We present experimental results on combinatorial<br>problems from MAP inference for Markov Random Fields, quadratic assignment and<br>cell tracking for developmental biology. Our highly parallel GPU implementation<br>improves upon the running times of the algorithms from Lange et al. (2021) by<br>up to an order of magnitude. In particular, we come close to or outperform some<br>state-of-the-art specialized heuristics while being problem agnostic.<br>

Explaining the decision of a multi-modal decision-maker requires to determine<br>the evidence from both modalities. Recent advances in XAI provide explanations<br>for models trained on still images. However, when it comes to modeling multiple<br>sensory modalities in a dynamic world, it remains underexplored how to<br>demystify the mysterious dynamics of a complex multi-modal model. In this work,<br>we take a crucial step forward and explore learnable explanations for<br>audio-visual recognition. Specifically, we propose a novel space-time attention<br>network that uncovers the synergistic dynamics of audio and visual data over<br>both space and time. Our model is capable of predicting the audio-visual video<br>events, while justifying its decision by localizing where the relevant visual<br>cues appear, and when the predicted sounds occur in videos. We benchmark our<br>model on three audio-visual video event datasets, comparing extensively to<br>multiple recent multi-modal representation learners and intrinsic explanation<br>models. Experimental results demonstrate the clear superior performance of our<br>model over the existing methods on audio-visual video event recognition.<br>Moreover, we conduct an in-depth study to analyze the explainability of our<br>model based on robustness analysis via perturbation tests and pointing games<br>using human annotations.<br>

Traditional domain adaptation addresses the task of adapting a model to a<br>novel target domain under limited or no additional supervision. While tackling<br>the input domain gap, the standard domain adaptation settings assume no domain<br>change in the output space. In semantic prediction tasks, different datasets<br>are often labeled according to different semantic taxonomies. In many<br>real-world settings, the target domain task requires a different taxonomy than<br>the one imposed by the source domain. We therefore introduce the more general<br>taxonomy adaptive domain adaptation (TADA) problem, allowing for inconsistent<br>taxonomies between the two domains. We further propose an approach that jointly<br>addresses the image-level and label-level domain adaptation. On the<br>label-level, we employ a bilateral mixed sampling strategy to augment the<br>target domain, and a relabelling method to unify and align the label spaces. We<br>address the image-level domain gap by proposing an uncertainty-rectified<br>contrastive learning method, leading to more domain-invariant and class<br>discriminative features. We extensively evaluate the effectiveness of our<br>framework under different TADA settings: open taxonomy, coarse-to-fine<br>taxonomy, and partially-overlapping taxonomy. Our framework outperforms<br>previous state-of-the-art by a large margin, while capable of adapting to<br>target taxonomies.<br>

Compositional Zero-Shot learning (CZSL) aims to recognize unseen compositions<br>of state and object visual primitives seen during training. A problem with<br>standard CZSL is the assumption of knowing which unseen compositions will be<br>available at test time. In this work, we overcome this assumption operating on<br>the open world setting, where no limit is imposed on the compositional space at<br>test time, and the search space contains a large number of unseen compositions.<br>To address this problem, we propose a new approach, Compositional Cosine Graph<br>Embeddings (Co-CGE), based on two principles. First, Co-CGE models the<br>dependency between states, objects and their compositions through a graph<br>convolutional neural network. The graph propagates information from seen to<br>unseen concepts, improving their representations. Second, since not all unseen<br>compositions are equally feasible, and less feasible ones may damage the<br>learned representations, Co-CGE estimates a feasibility score for each unseen<br>composition, using the scores as margins in a cosine similarity-based loss and<br>as weights in the adjacency matrix of the graphs. Experiments show that our<br>approach achieves state-of-the-art performances in standard CZSL while<br>outperforming previous methods in the open world scenario.<br>

Abstract<br>Humans are at the centre of a significant amount of research in computer vision.<br>Endowing machines with the ability to perceive people from visual data is an immense<br>scientific challenge with a high degree of direct practical relevance. Success in automatic<br>perception can be measured at different levels of abstraction, and this will depend on<br>which intelligent behaviour we are trying to replicate: the ability to localise persons in<br>an image or in the environment, understanding how persons are moving at the skeleton<br>and at the surface level, interpreting their interactions with the environment including<br>with other people, and perhaps even anticipating future actions. In this thesis we tackle<br>different sub-problems of the broad research area referred to as "looking at people",<br>aiming to perceive humans in images at different levels of granularity.<br>We start with bounding box-level pedestrian detection: We present a retrospective<br>analysis of methods published in the decade preceding our work, identifying various<br>strands of research that have advanced the state of the art. With quantitative exper-<br>iments, we demonstrate the critical role of developing better feature representations<br>and having the right training distribution. We then contribute two methods based<br>on the insights derived from our analysis: one that combines the strongest aspects of<br>past detectors and another that focuses purely on learning representations. The latter<br>method outperforms more complicated approaches, especially those based on hand-<br>crafted features. We conclude our work on pedestrian detection with a forward-looking<br>analysis that maps out potential avenues for future research.<br>We then turn to pixel-level methods: Perceiving humans requires us to both separate<br>them precisely from the background and identify their surroundings. To this end, we<br>introduce Cityscapes, a large-scale dataset for street scene understanding. This has since<br>established itself as a go-to benchmark for segmentation and detection. We additionally<br>develop methods that relax the requirement for expensive pixel-level annotations, focusing<br>on the task of boundary detection, i.e. identifying the outlines of relevant objects and<br>surfaces. Next, we make the jump from pixels to 3D surfaces, from localising and<br>labelling to fine-grained spatial understanding. We contribute a method for recovering<br>3D human shape and pose, which marries the advantages of learning-based and model-<br>based approaches.<br>We conclude the thesis with a detailed discussion of benchmarking practices in<br>computer vision. Among other things, we argue that the design of future datasets<br>should be driven by the general goal of combinatorial robustness besides task-specific<br>considerations.

Most learning methods for 3D data (point clouds, meshes) suffer significant<br>performance drops when the data is not carefully aligned to a canonical<br>orientation. Aligning real world 3D data collected from different sources is<br>non-trivial and requires manual intervention. In this paper, we propose the<br>Adjoint Rigid Transform (ART) Network, a neural module which can be integrated<br>with a variety of 3D networks to significantly boost their performance. ART<br>learns to rotate input shapes to a learned canonical orientation, which is<br>crucial for a lot of tasks such as shape reconstruction, interpolation,<br>non-rigid registration, and latent disentanglement. ART achieves this with<br>self-supervision and a rotation equivariance constraint on predicted rotations.<br>The remarkable result is that with only self-supervision, ART facilitates<br>learning a unique canonical orientation for both rigid and nonrigid shapes,<br>which leads to a notable boost in performance of aforementioned tasks. We will<br>release our code and pre-trained models for further research.<br>

Modern approaches to pose and body shape estimation have recently achieved<br>strong performance even under challenging real-world conditions. Even from a<br>single image of a clothed person, a realistic looking body shape can be<br>inferred that captures a users' weight group and body shape type well. This<br>opens up a whole spectrum of applications -- in particular in fashion -- where<br>virtual try-on and recommendation systems can make use of these new and<br>automatized cues. However, a realistic depiction of the undressed body is<br>regarded highly private and therefore might not be consented by most people.<br>Hence, we ask if the automatic extraction of such information can be<br>effectively evaded. While adversarial perturbations have been shown to be<br>effective for manipulating the output of machine learning models -- in<br>particular, end-to-end deep learning approaches -- state of the art shape<br>estimation methods are composed of multiple stages. We perform the first<br>investigation of different strategies that can be used to effectively<br>manipulate the automatic shape estimation while preserving the overall<br>appearance of the original image.<br>

Current datasets for video-based person re-identification (re-ID) do not<br>include structural knowledge in form of human pose annotations for the persons<br>of interest. Nonetheless, pose information is very helpful to disentangle<br>useful feature information from background or occlusion noise. Especially<br>real-world scenarios, such as surveillance, contain a lot of occlusions in<br>human crowds or by obstacles. On the other hand, video-based person re-ID can<br>benefit other tasks such as multi-person pose tracking in terms of robust<br>feature matching. For that reason, we present PoseTrackReID, a large-scale<br>dataset for multi-person pose tracking and video-based person re-ID. With<br>PoseTrackReID, we want to bridge the gap between person re-ID and multi-person<br>pose tracking. Additionally, this dataset provides a good benchmark for current<br>state-of-the-art methods on multi-frame person re-ID.<br>

Intuitively, image classification should profit from using spatial<br>information. Recent work, however, suggests that this might be overrated in<br>standard CNNs. In this paper, we are pushing the envelope and aim to further<br>investigate the reliance on spatial information. We propose spatial shuffling<br>and GAP+FC to destroy spatial information during both training and testing<br>phases. Interestingly, we observe that spatial information can be deleted from<br>later layers with small performance drops, which indicates spatial information<br>at later layers is not necessary for good performance. For example, test<br>accuracy of VGG-16 only drops by 0.03% and 2.66% with spatial information<br>completely removed from the last 30% and 53% layers on CIFAR100, respectively.<br>Evaluation on several object recognition datasets (CIFAR100, Small-ImageNet,<br>ImageNet) with a wide range of CNN architectures (VGG16, ResNet50, ResNet152)<br>shows an overall consistent pattern.<br>

Modern deep learning has enabled amazing developments of computer vision in recent years (Hinton and Salakhutdinov, 2006; Krizhevsky et al., 2012). As a fundamental task, semantic segmentation aims to predict class labels for each pixel of images, which empowers machines perception of the visual world. In spite of recent successes of fully convolutional networks (Long etal., 2015), several challenges remain to be addressed. In this thesis, we focus on this topic, under different kinds of input formats and various types of scenes. Specifically, our study contains two aspects: (1) Data-driven neural modules for improved performance. (2) Leverage of datasets w.r.t.training systems with higher performances and better data privacy guarantees. In the first part of this thesis, we improve semantic segmentation by designing new modules which are compatible with existing architectures. First, we develop a spatio-temporal data-driven pooling, which brings additional information of data (i.e. superpixels) into neural networks, benefiting the training of neural networks as well as the inference on novel data. We investigate our approach in RGB-D videos for segmenting indoor scenes, where depth provides complementary cues to colors and our model performs particularly well. Second, we design learnable dilated convolutions, which are the extension of standard dilated convolutions, whose dilation factors (Yu and Koltun, 2016) need to be carefully determined by hand to obtain decent performance. We present a method to learn dilation factors together with filter weights of convolutions to avoid a complicated search of dilation factors. We explore extensive studies on challenging street scenes, across various baselines with different complexity as well as several datasets at varying image resolutions. In the second part, we investigate how to utilize expensive training data. First, we start from the generative modelling and study the network architectures and the learning pipeline for generating multiple examples. We aim to improve the diversity of generated examples but also to preserve the comparable quality of the examples. Second, we develop a generative model for synthesizing features of a network. With a mixture of real images and synthetic features, we are able to train a segmentation model with better generalization capability. Our approach is evaluated on different scene parsing tasks to demonstrate the effectiveness of the proposed method. Finally, we study membership inference on the semantic segmentation task. We propose the first membership inference attack system against black-box semantic segmentation models, that tries to infer if a data pair is used as training data or not. From our observations, information on training data is indeed leaking. To mitigate the leakage, we leverage our synthetic features to perform prediction obfuscations, reducing the posterior distribution gaps between a training and a testing set. Consequently, our study provides not only an approach for detecting illegal use of data, but also the foundations for a safer use of semantic segmentation models.

Automatic detection of emergent leaders in small groups from nonverbal<br>behaviour is a growing research topic in social signal processing but existing<br>methods were evaluated on single datasets -- an unrealistic assumption for<br>real-world applications in which systems are required to also work in settings<br>unseen at training time. It therefore remains unclear whether current methods<br>for emergent leadership detection generalise to similar but new settings and to<br>which extent. To overcome this limitation, we are the first to study a<br>cross-dataset evaluation setting for the emergent leadership detection task. We<br>provide evaluations for within- and cross-dataset prediction using two current<br>datasets (PAVIS and MPIIGroupInteraction), as well as an investigation on the<br>robustness of commonly used feature channels (visual focus of attention, body<br>pose, facial action units, speaking activity) and online prediction in the<br>cross-dataset setting. Our evaluations show that using pose and eye contact<br>based features, cross-dataset prediction is possible with an accuracy of 0.68,<br>as such providing another important piece of the puzzle towards emergent<br>leadership detection in the real world.<br>

When labeled training data is scarce, a promising data augmentation approach is to generate visual features of unknown classes using their attributes. To learn the class conditional distribution of CNN features, these models rely on pairs of image features and class attributes. Hence, they can not make use of the abundance of unlabeled data samples. In this paper, we tackle any-shot learning problems i.e. zero-shot and few-shot, in a unified feature generating framework that operates in both inductive and transductive learning settings. We develop a conditional generative model that combines the strength of VAE and GANs and in addition, via an unconditional discriminator, learns the marginal feature distribution of unlabeled images. We empirically show that our model learns highly discriminative CNN features for five datasets, i.e. CUB, SUN, AWA and ImageNet, and establish a new state-of-the-art in any-shot learning, i.e. inductive and transductive (generalized) zero- and few-shot learning settings. We also demonstrate that our learned features are interpretable: we visualize them by inverting them back to the pixel space and we explain them by generating textual arguments of why they are associated with a certain label.

When labeled training data is scarce, a promising data augmentation approach is to generate visual features of unknown classes using their attributes. To learn the class conditional distribution of CNN features, these models rely on pairs of image features and class attributes. Hence, they can not make use of the abundance of unlabeled data samples. In this paper, we tackle any-shot learning problems i.e. zero-shot and few-shot, in a unified feature generating framework that operates in both inductive and transductive learning settings. We develop a conditional generative model that combines the strength of VAE and GANs and in addition, via an unconditional discriminator, learns the marginal feature distribution of unlabeled images. We empirically show that our model learns highly discriminative CNN features for five datasets, i.e. CUB, SUN, AWA and ImageNet, and establish a new state-of-the-art in any-shot learning, i.e. inductive and transductive (generalized) zero- and few-shot learning settings. We also demonstrate that our learned features are interpretable: we visualize them by inverting them back to the pixel space and we explain them by generating textual arguments of why they are associated with a certain label.

Due to the importance of zero-shot learning, i.e. classifying images where<br>there is a lack of labeled training data, the number of proposed approaches has<br>recently increased steadily. We argue that it is time to take a step back and<br>to analyze the status quo of the area. The purpose of this paper is three-fold.<br>First, given the fact that there is no agreed upon zero-shot learning<br>benchmark, we first define a new benchmark by unifying both the evaluation<br>protocols and data splits of publicly available datasets used for this task.<br>This is an important contribution as published results are often not comparable<br>and sometimes even flawed due to, e.g. pre-training on zero-shot test classes.<br>Moreover, we propose a new zero-shot learning dataset, the Animals with<br>Attributes 2 (AWA2) dataset which we make publicly available both in terms of<br>image features and the images themselves. Second, we compare and analyze a<br>significant number of the state-of-the-art methods in depth, both in the<br>classic zero-shot setting but also in the more realistic generalized zero-shot<br>setting. Finally, we discuss in detail the limitations of the current status of<br>the area which can be taken as a basis for advancing it.<br>

We present a simple yet effective method to infer detailed full human body<br>shape from only a single photograph. Our model can infer full-body shape<br>including face, hair, and clothing including wrinkles at interactive<br>frame-rates. Results feature details even on parts that are occluded in the<br>input image. Our main idea is to turn shape regression into an aligned<br>image-to-image translation problem. The input to our method is a partial<br>texture map of the visible region obtained from off-the-shelf methods. From a<br>partial texture, we estimate detailed normal and vector displacement maps,<br>which can be applied to a low-resolution smooth body model to add detail and<br>clothing. Despite being trained purely with synthetic data, our model<br>generalizes well to real-world photographs. Numerous results demonstrate the<br>versatility and robustness of our method.<br>

Internal thought refers to the process of directing attention away from a<br>primary visual task to internal cognitive processing. Internal thought is a<br>pervasive mental activity and closely related to primary task performance. As<br>such, automatic detection of internal thought has significant potential for<br>user modelling in intelligent interfaces, particularly for e-learning<br>applications. Despite the close link between the eyes and the human mind, only<br>a few studies have investigated vergence behaviour during internal thought and<br>none has studied moment-to-moment detection of internal thought from gaze.<br>While prior studies relied on long-term data analysis and required a large<br>number of gaze characteristics, we describe a novel method that is<br>computationally light-weight and that only requires eye vergence information<br>that is readily available from binocular eye trackers. We further propose a<br>novel paradigm to obtain ground truth internal thought annotations that<br>exploits human blur perception. We evaluate our method for three increasingly<br>challenging detection tasks: (1) during a controlled math-solving task, (2)<br>during natural viewing of lecture videos, and (3) during daily activities, such<br>as coding, browsing, and reading. Results from these evaluations demonstrate<br>the performance and robustness of vergence-based detection of internal thought<br>and, as such, open up new directions for research on interfaces that adapt to<br>shifts of mental attention.<br>

Recent methods to automatically calibrate stationary eye trackers were shown<br>to effectively reduce inherent calibration distortion. However, these methods<br>require additional information, such as mouse clicks or on-screen content. We<br>propose the first method that only requires users' eye movements to reduce<br>calibration distortion in the background while users naturally look at an<br>interface. Our method exploits that calibration distortion makes straight<br>saccade trajectories appear curved between the saccadic start and end points.<br>We show that this curving effect is systematic and the result of distorted gaze<br>projection plane. To mitigate calibration distortion, our method undistorts<br>this plane by straightening saccade trajectories using image warping. We show<br>that this approach improves over the common six-point calibration and is<br>promising for reducing distortion. As such, it provides a non-intrusive<br>solution to alleviating accuracy decrease of eye tracker during long-term use.<br>