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Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis

Don't Lie to Me! Robust and Efficient Explainability with Ve...

引用

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

作者： Fel, Thomas Ducoffe, Melanie Vigouroux, David Cadene, Remi Capelle, Mikael Nicodeme, Claire Serre, Thomas Brown Univ Carney Inst Brain Sci Providence RI 02912 USA Artificial & Nat Intelligence Toulouse Inst Toulouse France Sorbonne Univ CNRS Paris France IRT St Exupery Toulouse France SNCF Innovat & Res Div Paris France Thales Alenia Space Cannes France Airbus AI Res Blagnac France

ISBN: (纸本)9798350301298

A plethora of attribution methods have recently been developed to explain deep neural networks. These methods use different classes of perturbations (e.g, occlusion, blurring, masking, etc) to estimate the importance of individual image pixels to drive a model's decision. Nevertheless, the space of possible perturbations is vast and current attribution methods typically require significant computation time to accurately sample the space in order to achieve high-quality explanations. In this work, we introduce EVA (Explaining using Verified Perturbation Analysis) - the first explainability method which comes with guarantees that an entire set of possible perturbations has been exhaustively searched. We leverage recent progress in verified perturbation analysis methods to directly propagate bounds through a neural network to exhaustively probe a - potentially infinite-size - set of perturbations in a single forward pass. Our approach takes advantage of the beneficial properties of verified perturbation analysis, i.e., time efficiency and guaranteed complete - sampling agnostic coverage of the perturbation space - to identify image pixels that drive a model's decision. We evaluate EVA systematically and demonstrate state-of-the-art results on multiple benchmarks. Our code is freely available: ***/deel-ai/formal-explainability

关键词： Explainable computer vision

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H³Net: Irregular Posture Detection by Understanding Human Character and Core Structures

H<SUP>3</SUP>Net: Irregular Posture Detection by Understandi...

引用

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

作者： Noh, Seungha Bae, Kangmin Bae, Yuseok Lee, Byong-Dai Kyonggi Univ Suwon Kyonggi South Korea ETRI Daejeon South Korea

ISBN: (纸本)9798350365474

This paper proposes H3Net that considers detecting people in irregular postures by utilizing human structures and characters. To handle both features, we introduce two attention modules: 1) Human Structure Attention Module (HSAM), which is introduced to focus on the spatial aspects of a person, and 2) Human Character Attention Module (HCAM), which is designed to address the issue of repetitive appearance. HSAM effectively handles both foreground and background information about a human instance and utilizes keypoints to provide additional guidance to predict irregular postures. Meanwhile, HCAM employs ID information obtained from the tracking head, enriching the posture prediction with high-level semantic information. Furthermore, gathering images of people in irregular postures is a challenging task. Therefore, many conventional datasets consist of images with the same actors simulating varying postures in distinct images. To address this problem, we propose a Human ID Dependent Posture (HID2) loss that handles repeated instances. The HID2 loss generates a regularization term by considering duplicated instances to reduce bias. Our experiments demonstrate the effectiveness of H(3)Net compared to existing algorithms on irregular posture datasets. Furthermore, we show the qualitative results using color-coded masks and bounding boxes. We also provide ablation studies to highlight the significance of our proposed methods.

关键词： Character recognition

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Neural Dependencies Emerging from Learning Massive Categories

Neural Dependencies Emerging from Learning Massive Categorie...

引用

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

PointDistiller: Structured Knowledge Distillation Towards Ef...

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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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CapDet: Unifying Dense Captioning and Open-World Detection Pretraining

CapDet: Unifying Dense Captioning and Open-World Detection P...

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

作者： Long, Yanxin Wen, Youpeng Han, Jianhua Xu, Hang Ren, Pengzhen Zhang, Wei Zhao, Shen Liang, Xiaodan Sun Yat Sen Univ Shenzhen Campus Shenzhen Peoples R China Huawei Noahs Ark Lab Montreal PQ Canada MBZUAI Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350301298

Benefiting from large-scale vision-language pre-training on image-text pairs, open-world detection methods have shown superior generalization ability under the zero-shot or few-shot detection settings. However, a pre-defined category space is still required during the inference stage of existing methods and only the objects belonging to that space will be predicted. To introduce a "real" open-world detector, in this paper, we propose a novel method named CapDet to either predict under a given category list or directly generate the category of predicted bounding boxes. Specifically, we unify the open-world detection and dense caption tasks into a single yet effective framework by introducing an additional dense captioning head to generate the region-grounded captions. Besides, adding the captioning task will in turn benefit the generalization of detection performance since the captioning dataset covers more concepts. Experiment results show that by unifying the dense caption task, our CapDet has obtained significant performance improvements (e.g., +2.1% mAP on LVIS rare classes) over the baseline method on LVIS (1203 classes). Besides, our CapDet also achieves state-of-the-art performance on dense captioning tasks, e.g., 15.44% mAP on VG V1.2 and 13.98% on the VG-COCO dataset.

关键词： detection recognition: Categorization retrieval

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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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An In-depth Exploration of Person Re-identification and Gait recognition in Cloth-Changing Conditions

An In-depth Exploration of Person Re-identification and Gait...

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

作者： Li, Weijia Hou, Saihui Zhang, Chunjie Cao, Chunshui Liu, Xu Huang, Yongzhen Zhao, Yao Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China Beijing Normal Univ Sch Artificial Intelligence Beijing Peoples R China WATRIX AI Beijing Peoples R China Beijing Key Lab Adv Informat Sci & Network Beijing 100044 Peoples R China

ISBN: (纸本)9798350301298

The target of person re-identification (ReID) and gait recognition is consistent, that is to match the target pedestrian under surveillance cameras. For the cloth-changing problem, video-based ReID is rarely studied due to the lack of a suitable cloth-changing benchmark, and gait recognition is often researched under controlled conditions. To tackle this problem, we propose a Cloth-Changing benchmark for Person re-identification and Gait recognition (CCPG). It is a cloth-changing dataset, and there are several highlights in CCPG, (1) it provides 200 identities and over 16K sequences are captured indoors and outdoors, (2) each identity has seven different cloth-changing statuses, which is hardly seen in previous datasets, (3) RGB and silhouettes version data are both available for research purposes. Moreover, aiming to investigate the cloth-changing problem systematically, comprehensive experiments are conducted on video-based ReID and gait recognition methods. The experimental results demonstrate the superiority of ReID and gait recognition separately in different cloth-changing conditions and suggest that gait recognition is a potential solution for addressing the cloth-changing problem. Our dataset will be available at https://***/BNU-IVC/CCPG.

关键词： Datasets and evaluation

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Exploring Structured Semantic Prior for Multi Label recognition with Incomplete Labels

Exploring Structured Semantic Prior for Multi Label Recognit...

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

作者： Ding, Zixuan Wang, Ao Chen, Hui Zhang, Qiang Liu, Pengzhang Bao, Yongjun Yan, Weipeng Han, Jungong Xidian Univ Xian Peoples R China Tsinghua Univ Beijing Peoples R China BNRist Beijing Peoples R China Hangzhou Zhuoxi Inst Brain & Intelligence Hangzhou Peoples R China JD Com Beijing Peoples R China Univ Sheffield Dept Comp Sci Sheffield S Yorkshire England Univ Sheffield Ctr Machine Intelligence Sheffield S Yorkshire England

ISBN: (纸本)9798350301298

Multi-label recognition (MLR) with incomplete labels is very challenging. Recent works strive to explore the image-to-label correspondence in the vision-language model, i.e., CLIP [22], to compensate for insufficient annotations. In spite of promising performance, they generally overlook the valuable prior about the label-to-label correspondence. In this paper, we advocate remedying the deficiency of label supervision for the MLR with incomplete labels by deriving a structured semantic prior about the label-to-label correspondence via a semantic prior prompter. We then present a novel Semantic Correspondence Prompt Network (SCPNet), which can thoroughly explore the structured semantic prior. A Prior-Enhanced Self-Supervised Learning method is further introduced to enhance the use of the prior. Comprehensive experiments and analyses on several widely used benchmark datasets show that our method significantly outperforms existing methods on all datasets, well demonstrating the effectiveness and the superiority of our method. Our code will be available at https://***/jameslahm/SCPNet.

关键词： detection recognition: Categorization retrieval

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Teaching Structured vision & Language Concepts to vision & Language Models

Teaching Structured Vision & Language Concepts to Vision & L...

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

作者： Doveh, Sivan Arbelle, Assaf Harary, Sivan Schwartz, Eli Herzig, Roei Giryes, Raja Feris, Rogerio Panda, Rameswar Ullman, Shimon Karlinsky, Leonid IBM Res Yorktown Hts NY 10598 USA Weizmann Inst Sci Rehovot Israel Tel Aviv Univ Tel Aviv Israel MIT IBM Watson AI Lab Cambridge MA USA

ISBN: (纸本)9798350301298

vision and Language (VL) models have demonstrated remarkable zero-shot performance in a variety of tasks. However, some aspects of complex language understanding still remain a challenge. We introduce the collective notion of Structured vision & Language Concepts (SVLC) which includes object attributes, relations, and states which are present in the text and visible in the image. Recent studies have shown that even the best VL models struggle with SVLC. A possible way of fixing this issue is by collecting dedicated datasets for teaching each SVLC type, yet this might be expensive and time-consuming. Instead, we propose a more elegant data-driven approach for enhancing VL models' understanding of SVLCs that makes more effective use of existing VL pre-training datasets and does not require any additional data. While automatic understanding of image structure still remains largely unsolved, language structure is much better modeled and understood, allowing for its effective utilization in teaching VL models. In this paper, we propose various techniques based on language structure understanding that can be used to manipulate the textual part of off-the-shelf paired VL datasets. VL models trained with the updated data exhibit a significant improvement of up to 15% in their SVLC understanding with only a mild degradation in their zero-shot capabilities both when training from scratch or fine-tuning a pre-trained model. Our code and pretrained models are available at: https://***/SivanDoveh/TSVLC

关键词： Multi-modal learning

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Deep Deterministic Uncertainty: A New Simple Baseline

Deep Deterministic Uncertainty: A New Simple Baseline

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

作者： Mukhoti, Jishnu Kirsch, Andreas van Amersfoort, Joost Torr, Philip H. S. Gal, Yarin Univ Oxford Dept Comp Sci OATML Oxford England Univ Oxford Dept Engn Sci Torr Vis Grp Oxford England

ISBN: (纸本)9798350301298

Reliable uncertainty from deterministic single-forward pass models is sought after because conventional methods of uncertainty quantification are computationally expensive. We take two complex single-forward-pass uncertainty approaches, DUQ and SNGP, and examine whether they mainly rely on a well-regularized feature space. Crucially, without using their more complex methods for estimating uncertainty, we find that a single softmax neural net with such a regularized feature-space, achieved via residual connections and spectral normalization, outperforms DUQ and SNGP's epistemic uncertainty predictions using simple Gaussian Discriminant Analysis post-training as a separate feature-space density estimator-without fine-tuning on OoD data, feature ensembling, or input pre-procressing. Our conceptually simple Deep Deterministic Uncertainty (DDU) baseline can also be used to disentangle aleatoric and epistemic uncertainty and performs as well as Deep Ensembles, the stateof-the art for uncertainty prediction, on several OoD benchmarks (CIFAR-10/100 vs SVHN/Tiny-ImageNet, ImageNet vs ImageNet-O), active learning settings across different model architectures, as well as in large scale vision tasks like semantic segmentation, while being computationally cheaper.

关键词： accountability ethics in vision fairness privacy Transparency

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