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Trade-off between Robustness and Accuracy of vision Transformers

Trade-off between Robustness and Accuracy of Vision Transfor...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Li, Yanxi Xu, Chang Univ Sydney Fac Engn Sch Comp Sci Sydney NSW Australia

ISBN: (纸本)9798350301298

Although deep neural networks (DNNs) have shown great successes in computer vision tasks, they are vulnerable to perturbations on inputs, and there exists a trade-off between the natural accuracy and robustness to such perturbations, which is mainly caused by the existence of robust non-predictive features and non-robust predictive features. Recent empirical analyses find vision Transformers (ViTs) are inherently robust to various kinds of perturbations, but the aforementioned trade-off still exists for them. In this work, we propose Trade-off between Robustness and Accuracy of vision Transformers (TORA-ViTs), which aims to efficiently transfer ViT models pretrained on natural tasks for both accuracy and robustness. TORA-ViTs consist of two major components, including a pair of accuracy and robustness adapters to extract predictive and robust features, respectively, and a gated fusion module to adjust the trade-off. The gated fusion module takes outputs of a pretrained ViT block as queries and outputs of our adapters as keys and values, and tokens from different adapters at different spatial locations are compared with each other to generate attention scores for a balanced mixing of predictive and robust features. Experiments on ImageNet with various robust benchmarks show that our TORA-ViTs can efficiently improve the robustness of naturally pretrained ViTs while maintaining competitive natural accuracy. Our most balanced setting (TORA-ViTs with lambda = 0.5) can maintain 83.7% accuracy on clean ImageNet and reach 54.7% and 38.0% accuracy under FGSM and PGD white-box attacks, respectively. In terms of various ImageNet variants, it can reach 39.2% and 56.3% accuracy on ImageNet-A and ImageNet-R and reach 34.4% mCE on ImageNet-C.

关键词： Adversarial attack and defense

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PointDistiller: Structured Knowledge Distillation Towards Efficient and Compact 3D Detection

PointDistiller: Structured Knowledge Distillation Towards Ef...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Linfeng Dong, Runpei Tai, Hung-Shuo Ma, Kaisheng Tsinghua Univ Beijing Peoples R China Xi An Jiao Tong Univ Xian Peoples R China DIDI Beijing Peoples R China

ISBN: (纸本)9798350301298

The remarkable breakthroughs in point cloud representation learning have boosted their usage in real-world applications such as self-driving cars and virtual reality. However, these applications usually have a strict requirement for not only accurate but also efficient 3D object detection. Recently, knowledge distillation has been proposed as an effective model compression technique, which transfers the knowledge from an over-parameterized teacher to a lightweight student and achieves consistent effectiveness in 2D vision. However, due to point clouds' sparsity and irregularity, directly applying previous image-based knowledge distillation methods to point cloud detectors usually leads to unsatisfactory performance. To fill the gap, this paper proposes PointDistiller, a structured knowledge distillation framework for point clouds-based 3D detection. Concretely, PointDistiller includes local distillation which extracts and distills the local geometric structure of point clouds with dynamic graph convolution and reweighted learning strategy, which highlights student learning on the crucial points or voxels to improve knowledge distillation efficiency. Extensive experiments on both voxels-based and raw points-based detectors have demonstrated the effectiveness of our method over seven previous knowledge distillation methods. For instance, our 4x compressed PointPillars student achieves 2.8 and 3.4 mAP improvements on BEV and 3D object detection, outperforming its teacher by 0.9 and 1.8 mAP, respectively. Codes are available in https://***/RunpeiDong/PointDistiller.

关键词： Efficient and scalable vision

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Self-Supervised Learning for Place Representation Generalization across Appearance Changes

Self-Supervised Learning for Place Representation Generaliza...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Musallam, Mohamed Adel Gaudilliere, Vincent Aouada, Djamila Univ Luxembourg SnT Esch Sur Alzette Luxembourg

ISBN: (纸本)9798350318920;9798350318937

Visual place recognition is a key to unlocking spatial navigation for animals, humans and robots. While state-of-the-art approaches are trained in a supervised manner and therefore hardly capture the information needed for generalizing to unusual conditions, we argue that self-supervised learning may help abstracting the place representation so that it can be foreseen, irrespective of the conditions. More precisely, in this paper, we investigate learning features that are robust to appearance modifications while sensitive to geometric transformations in a self-supervised manner. This dual-purpose training is made possible by combining the two self-supervision main paradigms, i.e. contrastive and predictive learning. Our results on standard benchmarks reveal that jointly learning such appearance-robust and geometry-sensitive image descriptors leads to competitive visual place recognition results across adverse seasonal and illumination conditions, without requiring any human-annotated labels.(1).

关键词： Algorithms Applications Autonomous Driving Image recognition and understanding

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Rethinking Image Super Resolution from Long-Tailed Distribution Learning Perspective

Rethinking Image Super Resolution from Long-Tailed Distribut...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Gou, Yuanbiao Hu, Peng Lv, Jiancheng Zhu, Hongyuan Peng, Xi Sichuan Univ Coll Comp Sci Chengdu Peoples R China ASTAR Inst Infocomm Res I2R Singapore Singapore

ISBN: (纸本)9798350301298

Existing studies have empirically observed that the resolution of the low-frequency region is easier to enhance than that of the high-frequency one. Although plentiful works have been devoted to alleviating this problem, little understanding is given to explain it. In this paper, we try to give a feasible answer from a machine learning perspective, i.e., the twin fitting problem caused by the long-tailed pixel distribution in natural images. With this explanation, we reformulate image super resolution (SR) as a long-tailed distribution learning problem and solve it by bridging the gaps of the problem between in low- and high-level vision tasks. As a result, we design a long-tailed distribution learning solution, that rebalances the gradients from the pixels in the low- and high-frequency region, by introducing a static and a learnable structure prior. The learned SR model achieves better balance on the fitting of the low- and high-frequency region so that the overall performance is improved. In the experiments, we evaluate the solution on four CNN- and one Transformer-based SR models w.r.t. six datasets and three tasks, and experimental results demonstrate its superiority.

关键词： Low-level vision

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Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution

Learning Spatial-Temporal Implicit Neural Representations fo...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Lu, Yunfan Wang, Zipeng Liu, Minjie Wang, Hongjian Wang, Lin HKUST GZ AI Thrust Guangzhou Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Beijing Peoples R China HKUST Dept Comp Sci & Engn Guangzhou Peoples R China

ISBN: (纸本)9798350301298

Event cameras sense the intensity changes asynchronously and produce event streams with high dynamic range and low latency. This has inspired research endeavors utilizing events to guide the challenging video super-resolution (VSR) task. In this paper, we make the first attempt to address a novel problem of achieving VSR at random scales by taking advantages of the high temporal resolution property of events. This is hampered by the difficulties of representing the spatial-temporal information of events when guiding VSR. To this end, we propose a novel framework that incorporates the spatial-temporal interpolation of events to VSR in a unified framework. Our key idea is to learn implicit neural representations from queried spatial-temporal coordinates and features from both RGB frames and events. Our method contains three parts. Specifically, the Spatial-Temporal Fusion (STF) module first learns the 3D features from events and RGB frames. Then, the Temporal Filter (TF) module unlocks more explicit motion information from the events near the queried timestamp and generates the 2D features. Lastly, the Spatial-Temporal Implicit Representation (STIR) module recovers the SR frame in arbitrary resolutions from the outputs of these two modules. In addition, we collect a real-world dataset with spatially aligned events and RGB frames. Extensive experiments show that our method significantly surpasses the prior-arts and achieves VSR with random scales, e.g., 6.5. Code and dataset are available at https: //***/cvpr23/egvsr.

关键词： Low-level vision

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Overlooked Factors in Concept-based Explanations: Dataset Choice, Concept Learnability, and Human Capability

Overlooked Factors in Concept-based Explanations: Dataset Ch...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Ramaswamy, Vikram V. Kim, Sunnie S. Y. Fong, Ruth Russakovsky, Olga Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9798350301298

Concept-based interpretability methods aim to explain a deep neural network model's components and predictions using a pre-defined set of semantic concepts. These methods evaluate a trained model on a new, "probe" dataset and correlate the model's outputs with concepts labeled in that dataset. Despite their popularity, they suffer from limitations that are not well-understood and articulated in the literature. In this work, we identify and analyze three commonly overlooked factors in concept-based explanations. First, we find that the choice of the probe dataset has a profound impact on the generated explanations. Our analysis reveals that different probe datasets lead to very different explanations, suggesting that the generated explanations are not generalizable outside the probe dataset. Second, we find that concepts in the probe dataset are often harder to learn than the target classes they are used to explain, calling into question the correctness of the explanations. We argue that only easily learnable concepts should be used in concept-based explanations. Finally, while existing methods use hundreds or even thousands of concepts, our human studies reveal a much stricter upper bound of 32 concepts or less, beyond which the explanations are much less practically useful. We discuss the implications of our findings and provide suggestions for future development of concept-based interpretability methods. Code for our analysis and user interface can be found at https://***/princetonvisualai/OverlookedFactors

关键词： Explainable computer vision

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Neural Dependencies Emerging from Learning Massive Categories

Neural Dependencies Emerging from Learning Massive Categorie...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Feng, Ruili Zheng, Kecheng Zhu, Kai Shen, Yujun Zhao, Jian Huang, Yukun Zhao, Deli Zhou, Jingren Jordan, Michael Zha, Zheng-Jun Univ Sci & Technol China Hefei Peoples R China Ant Grp Hangzhou Peoples R China Alibaba Grp Hangzhou Peoples R China Univ Calif Berkeley Berkeley CA USA

ISBN: (纸本)9798350301298

This work presents two astonishing findings on neural networks learned for large-scale image classification. 1) Given a well-trained model, the logits predicted for some category can be directly obtained by linearly combining the predictions of a few other categories, which we call neural dependency. 2) Neural dependencies exist not only within a single model, but even between two independently learned models, regardless of their architectures. Towards a theoretical analysis of such phenomena, we demonstrate that identifying neural dependencies is equivalent to solving the Covariance Lasso (CovLasso) regression problem proposed in this paper. Through investigating the properties of the problem solution, we confirm that neural dependency is guaranteed by a redundant logit covariance matrix, which condition is easily met given massive categories, and that neural dependency is highly sparse, implying that one category correlates to only a few others. We further empirically show the potential of neural dependencies in understanding internal data correlations, generalizing models to unseen categories, and improving model robustness with a dependency-derived regularizer. Code to reproduce the results in this paper is available at https://***/RuiLiFeng/Neural-Dependencies.

关键词： computer vision theory

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PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation Layout

PosterLayout: A New Benchmark and Approach for Content-aware...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Hsu, Hsiao Yuan He, Xiangteng Peng, Yuxin Kong, Hao Zhang, Qing Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China Peking Univ Sch Comp Sci Natl Key Lab Multimedia Informat Proc Beijing Peoples R China Meituan Beijing Peoples R China

ISBN: (纸本)9798350301298

Content-aware visual-textual presentation layout aims at arranging spatial space on the given canvas for pre-defined elements, including text, logo, and underlay, which is a key to automatic template-free creative graphic design. In practical applications, e.g., poster designs, the canvas is originally non-empty, and both inter-element relationships as well as inter-layer relationships should be concerned when generating a proper layout. A few recent works deal with them simultaneously, but they still suffer from poor graphic performance, such as a lack of layout variety or spatial non-alignment. Since content-aware visual-textual presentation layout is a novel task, we first construct a new dataset named PKU PosterLayout, which consists of 9,974 poster-layout pairs and 905 images, i.e., non-empty canvases. It is more challenging and useful for greater layout variety, domain diversity, and content diversity. Then, we propose design sequence formation (DSF) that reorganizes elements in layouts to imitate the design processes of human designers, and a novel CNN-LSTM-based conditional generative adversarial network (GAN) is presented to generate proper layouts. Specifically, the discriminator is design-sequence-aware and will supervise the "design" process of the generator. Experimental results verify the usefulness of the new benchmark and the effectiveness of the proposed approach, which achieves the best performance by generating suitable layouts for diverse canvases. The dataset and the source code are available at https://***/PKU-ICSTMIPL/PosterLayout-cvpr2023.

关键词： vision applications and systems

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Mitigating Demographic Bias in Face recognition via Regularized Score Calibration

Mitigating Demographic Bias in Face Recognition via Regulari...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Kotwal, Ketan Marcel, Sebastien Idiap Res Inst Martigny Switzerland Univ Lausanne Lausanne Switzerland

ISBN: (纸本)9798350370287;9798350370713

Demographic bias in deep learning-based face recognition systems has led to serious concerns. Several existing works attempt to mitigate bias by incorporating demographic-specific processing during inference, which requires knowledge or learning of demographic attribute with an additional cost. We propose to regularize training of the face recognition CNN, for demographic fairness, by imposing constraints on the distributions of matching scores. Our regularization term enforces the score distributions from different demographic groups to respect a pre-defined probability distribution, as well as it penalizes misalignment of distributions across demographic groups. The proposed method improves fairness of face recognition models without compromising the recognition accuracy, and does not require extra resources during inference. Our experiments indicate that in a cross-dataset testing, the regularized CNN can reduce the variation in accuracies (i.e., more fairness) of different demographic groups up to 25% while slightly improving recognition accuracy over baselines.

关键词： Face recognition

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Improving Commonsense in vision-Language Models via Knowledge Graph Riddles

Improving Commonsense in Vision-Language Models via Knowledg...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Ye, Shuquan Xie, Yujia Chen, Dongdong Xu, Yichong Yuan, Lu Zhu, Chenguang Liao, Jing Microsoft Redmond WA USA City Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350301298

This paper focuses on analyzing and improving the commonsense ability of recent popular vision-language (VL) models. Despite the great success, we observe that existing VL-models still lack commonsense knowledge/reasoning ability (e.g., "Lemons are sour"), which is a vital component towards artificial general intelligence. Through our analysis, we find one important reason is that existing large-scale VL datasets do not contain much commonsense knowledge, which motivates us to improve the commonsense of VL-models from the data perspective. Rather than collecting a new VL training dataset, we propose a more scalable strategy, i.e., "Data Augmentation with kNowledge graph linearization for CommonsensE capability" (DANCE). It can be viewed as one type of data augmentation technique, which can inject commonsense knowledge into existing VL datasets on the fly during training. More specifically, we leverage the commonsense knowledge graph (e.g., ConceptNet) and create variants of text description in VL datasets via bidirectional sub-graph sequentialization. For better commonsense evaluation, we further propose the first retrieval-based commonsense diagnostic benchmark. By conducting extensive experiments on some representative VL-models, we demonstrate that our DANCE technique is able to significantly improve the commonsense ability while maintaining the performance on vanilla retrieval tasks. The code and data are available at https:// ***/ pleaseconnectwifi/ DANCE.

关键词： Multi-modal learning

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