D2
Computer Vision and Machine Learning
2023
Class-Incremental Exemplar Compression for Class-Incremental Learning
Z. Luo, Y. Liu, B. Schiele and Q. Sun
36th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023), 2023
(Accepted/in press)
A Polyhedral Study of Lifted Multicuts
B. Andres, S. Di Gregorio, J. Irmai and J.-H. Lange
Discrete Optimization, Volume 47, 2023
SoftMatch: Addressing the Quantity-Quality Tradeoff in Semi-supervised Learning
H. Chen, R. Tao, Y. Fan, Y. Wang, M. Savvides, J. Wang, B. Raj, X. Xie and B. Schiele
Eleventh International Conference on Learning Representations (ICLR 2023), 2023
(Accepted/in press)
Neural Architecture Design and Robustness: A Dataset
S. Jung, J. Lukasik and M. Keuper
Eleventh International Conference on Learning Representations (ICLR 2023), 2023
(Accepted/in press)
Abstract
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%.
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
Y. Wang, H. Chen, Q. Heng, W. Hou, Y. Fan, Z. Wu, J. Wang, M. Savvides, T. Shinozaki, B. Raj, B. Schiele and X. Xie
Eleventh International Conference on Learning Representations (ICLR 2023), 2023
(Accepted/in press)
Weakly-Supervised Domain Adaptive Semantic Segmentation With Prototypical Contrastive Learning
A. Das, Y. Xian, D. Dai and B. Schiele
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023), 2023
(Accepted/in press)
HGFormer: Hierarchical Grouping Transformer for Domain Generalized Semantic Segmentation
J. Ding, N. Xue, G.-S. Xia, B. Schiele and D. Dai
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023), 2023
(Accepted/in press)
Federated Incremental Semantic Segmentation
J. Dong, D. Zhang, Y. Cong, W. Cong, H. Ding and D. Dai
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023), 2023
(Accepted/in press)
Continuous Pseudo-Label Rectified Domain Adaptive Semantic Segmentation With Implicit Neural Representations
R. Gong, Q. Wang, M. Danelljan, D. Dai and L. Van Gool
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023), 2023
(Accepted/in press)
Improving Robustness of Vision Transformers by Reducing Sensitivity To Patch Corruptions
Y. Guo, D. Stutz and B. Schiele
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023), 2023
(Accepted/in press)
MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation
L. Hoyer, D. Dai, H. Wang and L. Van Gool
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023), 2023
(Accepted/in press)
A Meta-Learning Approach to Predicting Performance and Data Requirements
A. Jain, G. Swaminathan, P. Favaro, H. Yang, A. Ravichandran, H. Harutyunyan, A. Achille, O. Dabeer, B. Schiele, A. Swaminathan and S. Soatto
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023), 2023
(Accepted/in press)
Self-Supervised Pre-Training With Masked Shape Prediction for 3D Scene Understanding
L. Jiang, Z. Yang, S. Shi, V. Golyanik, D. Dai and B. Schiele
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023), 2023
(Accepted/in press)
Continual Detection Transformer for Incremental Object Detection
Y. Liu, B. Schiele, A. Vedaldi and C. Rupprecht
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023), 2023
(Accepted/in press)
Object Pop-Up: Can We Infer 3D Objects and their Poses from Human Interactions Alone?
I. A. Petrov, R. Marin, J. Chibane and G. Pons-Moll
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023), 2023
(Accepted/in press)
DSVT: Dynamic Sparse Voxel Transformer With Rotated Sets
H. Wang, C. Shi, S. Shi, M. Lei, S. Wang, D. He, B. Schiele and L. Wang
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023), 2023
(Accepted/in press)
Visibility Aware Human-Object Interaction Tracking from Single RGB Camera
X. Xie, B. L. Bhatnagar and G. Pons-Moll
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023), 2023
(Accepted/in press)
Binaural SoundNet: Predicting Semantics, Depth and Motion with Binaural Sounds
D. Dai, A. B. Vasudevan, J. Matas and L. Van Gool
IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 45, Number 1, 2023
A Deeper Look into DeepCap
M. Habermann, W. Xu, M. Zollhöfer, G. Pons-Moll and C. Theobalt
IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 45, Number 4, 2023
Abstract
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>
Higher-Order Multicuts for Geometric Model Fitting and Motion Segmentation
E. Levinkov, A. Kardoost, B. Andres and M. Keuper
IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 45, Number 1, 2023
Abstract
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.
Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators
D. Stutz, N. Chandramoorthy, M. Hein and B. Schiele
IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 45, Number 3, 2023
Urban Scene Semantic Segmentation With Low-Cost Coarse Annotation
A. Das, Y. Xian, Y. He, Z. Akata and B. Schiele
2023 IEEE Winter Conference on Applications of Computer Vision (WACV 2023), 2023
Control-NeRF: Editable Feature Volumes for Scene Rendering and Manipulation
V. Lazova, V. Guzov, K. Olszewski, S. Tulyakov and G. Pons-Moll
2023 IEEE Winter Conference on Applications of Computer Vision (WACV 2023), 2023
Jointly Learning Band Selection and Filter Array Design for Hyperspectral Imaging
K. Li, D. Dai and L. Van Gool
2023 IEEE Winter Conference on Applications of Computer Vision (WACV 2023), 2023
Intra-Source Style Augmentation for Improved Domain Generalization
Y. Li, D. Zhang, M. Keuper and A. Khoreva
2023 IEEE Winter Conference on Applications of Computer Vision (WACV 2023), 2023
Revisiting Consistency Regularization for Semi-supervised Learning
Y. Fan, A. Kukleva, D. Dai and B. Schiele
International Journal of Computer Vision, Volume 131, 2023
Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
L. Hoyer, D. Dai, Q. Wang, Y. Chen and L. Van Gool
International Journal of Computer Vision, 2023
3D Object Detection for Autonomous Driving: A Comprehensive Survey
J. Mao, S. Shi, X. Wang and H. Li
International Journal of Computer Vision, 2023
Learning Comprehensive Global Features in Person Re-identification: Ensuring Discriminativeness of more Local Regions
J. Xi, J. Huang, S. Zheng, Q. Zhou, B. Schiele, X.-S. Hua and Q. Sun
Pattern Recognition, Volume 134, 2023
Online Hyperparameter Optimization for Class-Incremental Learning
Y. Liu, Y. Li, B. Schiele and Q. Sun
Proceedings of the 37th AAAI Conference on Artificial Intelligence, 2023
(Accepted/in press)
Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering
D. M. H. Nguyen, H. Nguyen, M. T. N. Truong, T. Cao, B. T. Nguyen, N. Ho, P. Swoboda, S. Albarqouni, P. Xie and D. Sonntag
Proceedings of the 37th AAAI Conference on Artificial Intelligence, 2023
(Accepted/in press)
Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences
X. Hong, A. Sayeed, K. Mehra, V. Demberg and B. Schiele
Transactions of the Association for Computational Linguistics, Volume 11, 2023
Multi-Task Learning using Transformer
L. H. Abdel Khaliq
PhD Thesis, Universität des Saarlandes, 2023
Modelling 3D Humans : Pose, Shape, Clothing and Interactions
B. L. Bhatnagar
PhD Thesis, Universität des Saarlandes, 2023
Few-Shot Learning with the Help of Self-Supervision
J. Gabelmann
PhD Thesis, Universität des Saarlandes, 2023
Semantic Road Scene Understanding with Realistic Synthetic Data
A. Jadon
PhD Thesis, Universität des Saarlandes, 2023
Learning from Imperfect Data Incremental Learning and Few-shot Learning
Y. Liu
PhD Thesis, Universität des Saarlandes, 2023
Improving Quality and Controllability in GAN-based Image Synthesis
E. Schönfeld
PhD Thesis, Universität des Saarlandes, 2023