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Diversity-Measurable Anomaly Detection

Diversity-Measurable Anomaly Detection

引用

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

作者： Liu, Wenrui Chang, Hong Ma, Bingpeng Shan, Shiguang Chen, Xilin Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798350301298

Reconstruction-based anomaly detection models achieve their purpose by suppressing the generalization ability for anomaly. However, diverse normal patterns are consequently not well reconstructed as well. Although some efforts have been made to alleviate this problem by modeling sample diversity, they suffer from shortcut learning due to undesired transmission of abnormal information. In this paper, to better handle the tradeoff problem, we propose Diversity-Measurable Anomaly Detection (DMAD) framework to enhance reconstruction diversity while avoid the undesired generalization on anomalies. To this end, we design Pyramid Deformation Module (PDM), which models diverse normals and measures the severity of anomaly by estimating multi-scale deformation fields from reconstructed reference to original input. Integrated with an information compression module, PDM essentially decouples deformation from prototypical embedding and makes the final anomaly score more reliable. Experimental results on both surveillance videos and industrial images demonstrate the effectiveness of our method. In addition, DMAD works equally well in front of contaminated data and anomaly-like normal samples.

关键词： detection recognition: Categorization retrieval

来源：评论

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Learning to Generate Image Embeddings with User-level Differential Privacy

Learning to Generate Image Embeddings with User-level Differ...

引用

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

作者： Xu, Zheng Collins, Maxwell Wang, Yuxiao Panait, Liviu Oh, Sewoong Augenstein, Sean Liu, Ting Schroff, Florian McMahan, H. Brendan Google Res Mountain View CA 94043 USA

ISBN: (纸本)9798350301298

Small on-device models have been successfully trained with user-level differential privacy (DP) for next word prediction and image classification tasks in the past. However, existing methods can fail when directly applied to learn embedding models using supervised training data with a large class space. To achieve user-level DP for large image-to-embedding feature extractors, we propose DP-FedEmb, a variant of federated learning algorithms with per-user sensitivity control and noise addition, to train from user-partitioned data centralized in the datacenter. DP-FedEmb combines virtual clients, partial aggregation, private local fine-tuning, and public pretraining to achieve strong privacy utility trade-offs. We apply DP-FedEmb to train image embedding models for faces, landmarks and natural species, and demonstrate its superior utility under same privacy budget on benchmark datasets DigiFace, EMNIST, GLD and iNaturalist. We further illustrate it is possible to achieve strong user-level DP guarantees of epsilon < 2 while controlling the utility drop within 5%, when millions of users can participate in training

关键词： accountability ethics in vision fairness privacy Transparency

来源：评论

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TINC: Tree-structured Implicit Neural Compression

TINC: Tree-structured Implicit Neural Compression

引用

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

作者： Yang, Runzhao Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9798350301298

Implicit neural representation (INR) can describe the target scenes with high fidelity using a small number of parameters, and is emerging as a promising data compression technique. However, limited spectrum coverage is intrinsic to INR, and it is non-trivial to remove redundancy in diverse complex data effectively. Preliminary studies can only exploit either global or local correlation in the target data and thus of limited performance. In this paper, we propose a Tree-structured Implicit Neural Compression (TINC) to conduct compact representation for local regions and extract the shared features of these local representations in a hierarchical manner. Specifically, we use Multi-Layer Perceptrons (MLPs) to fit the partitioned local regions, and these MLPs are organized in tree structure to share parameters according to the spatial distance. The parameter sharing scheme not only ensures the continuity between adjacent regions, but also jointly removes the local and non-local redundancy. Extensive experiments show that TINC improves the compression fidelity of INR, and has shown impressive compression capabilities over commercial tools and other deep learning based methods. Besides, the approach is of high flexibility and can be tailored for different data and parameter settings. The source code can be found at https://***/RichealYoung/TINC.

关键词： cell microscopy Medical and biological vision

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Re-thinking Federated Active Learning based on Inter-class Diversity

Re-thinking Federated Active Learning based on Inter-class D...

引用

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

作者： Kim, SangMook Bae, Sangmin Song, Hwanjun Yun, Se -Young KAIST AI Daejeon South Korea NAVER AI LAB Grenoble France

ISBN: (纸本)9798350301298

Although federated learning has made awe-inspiring advances, most studies have assumed that the client's data are fully labeled. However, in a real-world scenario, every client may have a significant amount of unlabeled instances. Among the various approaches to utilizing unlabeled data, a federated active learning framework has emerged as a promising solution. In the decentralized setting, there are two types of available query selector models, namely 'global' and 'local-only' models, but little literature discusses their performance dominance and its causes. In this work, we first demonstrate that the superiority of two selector models depends on the global and local interclass diversity. Furthermore, we observe that the global and local-only models are the keys to resolving the imbalance of each side. Based on our findings, we propose LoGo, a FAL sampling strategy robust to varying local heterogeneity levels and global imbalance ratio, that integrates both models by two steps of active selection scheme. LoGo consistently outperforms six active learning strategies in the total number of 38 experimental settings. The code is available at: https://***/raymin0223/LoGo.

关键词： vision applications and systems

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Generative Bias for Robust Visual Question Answering

Generative Bias for Robust Visual Question Answering

引用

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

作者： Cho, Jae Won Kim, Dong-Jin Ryu, Hyeonggon Kweon, In So Korea Adv Inst Sci & Technol Daejeon South Korea Hanyang Univ Seoul South Korea

ISBN: (纸本)9798350301298

The task of Visual Question Answering (VQA) is known to be plagued by the issue of VQA models exploiting biases within the dataset to make its final prediction. Various previous ensemble based debiasing methods have been proposed where an additional model is purposefully trained to be biased in order to train a robust target model. However, these methods compute the bias for a model simply from the label statistics of the training data or from single modal branches. In this work, in order to better learn the bias a target VQA model suffers from, we propose a generative method to train the bias model directly from the target model, called GenB. In particular, GenB employs a generative network to learn the bias in the target model through a combination of the adversarial objective and knowledge distillation. We then debias our target model with GenB as a bias model, and show through extensive experiments the effects of our method on various VQA bias datasets including VQA-CP2, VQA-CP1, GQA-OOD, and VQA-CE, and show state-of-the-art results with the LXMERT architecture on VQA-CP2.

关键词： language reasoning vision

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CutMIB: Boosting Light Field Super-Resolution via Multi-View Image Blending

CutMIB: Boosting Light Field Super-Resolution via Multi-View...

引用

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

作者： Xiao, Zeyu Liu, Yutong Gao, Ruisheng Xiong, Zhiwei Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350301298

Data augmentation (DA) is an efficient strategy for improving the performance of deep neural networks. Recent DA strategies have demonstrated utility in single image super-resolution (SR). Little research has, however, focused on the DA strategy for light field SR, in which multi-view information utilization is required. For the first time in light field SR, we propose a potent DA strategy called CutMIB to improve the performance of existing light field SR networks while keeping their structures unchanged. Specifically, CutMIB first cuts low-resolution (LR) patches from each view at the same location. Then CutMIB blends all LR patches to generate the blended patch and finally pastes the blended patch to the corresponding regions of high-resolution light field views, and vice versa. By doing so, CutMIB enables light field SR networks to learn from implicit geometric information during the training stage. Experimental results demonstrate that CutMIB can improve the reconstruction performance and the angular consistency of existing light field SR networks. We further verify the effectiveness of CutMIB on real-world light field SR and light field denoising. The implementation code is available at https://***/zeyuxiao1997/CutMIB.

关键词： Low-level vision

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SViTT: Temporal Learning of Sparse Video-Text Transformers

SViTT: Temporal Learning of Sparse Video-Text Transformers

引用

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

作者： Li, Yi Min, Kyle Tripathi, Subarna Vasconcelos, Nuno Univ Calif San Diego La Jolla CA 92093 USA Intel Labs Santa Clara CA USA

ISBN: (纸本)9798350301298

Do video-text transformers learn to model temporal relationships across frames? Despite their immense capacity and the abundance of multimodal training data, recent work has revealed the strong tendency of video-text models towards frame-based spatial representations, while temporal reasoning remains largely unsolved. In this work, we identify several key challenges in temporal learning of video-text transformers: the spatiotemporal trade-off from limited network size;the curse of dimensionality for multi-frame modeling;and the diminishing returns of semantic information by extending clip length. Guided by these findings, we propose SViTT, a sparse video-text architecture that performs multi-frame reasoning with significantly lower cost than naive transformers with dense attention. Analogous to graph-based networks, SViTT employs two forms of sparsity: edge sparsity that limits the query-key communications between tokens in self-attention, and node sparsity that discards uninformative visual tokens. Trained with a curriculum which increases model sparsity with the clip length, SViTT outperforms dense transformer baselines on multiple video-text retrieval and question answering benchmarks, with a fraction of computational cost. Project page: http://***/projects/svitt.

关键词： and reasoning language vision

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SFD2: Semantic-guided Feature Detection and Description

SFD2: Semantic-guided Feature Detection and Description

引用

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

作者： Xue, Fei Budvytis, Ignas Cipolla, Roberto Univ Cambridge Cambridge England

ISBN: (纸本)9798350301298

Visual localization is a fundamental task for various applications including autonomous driving and robotics. Prior methods focus on extracting large amounts of often redundant locally reliable features, resulting in limited efficiency and accuracy, especially in large-scale environments under challenging conditions. Instead, we propose to extract globally reliable features by implicitly embedding high-level semantics into both the detection and description processes. Specifically, our semantic-aware detector is able to detect keypoints from reliable regions (e.g. building, traffic lane) and suppress unreliable areas (e.g. sky, car) implicitly instead of relying on explicit semantic labels. This boosts the accuracy of keypoint matching by reducing the number of features sensitive to appearance changes and avoiding the need of additional segmentation networks at test time. Moreover, our descriptors are augmented with semantics and have stronger discriminative ability, providing more inliers at test time. Particularly, experiments on long-term large-scale visual localization Aachen Day-Night and RobotCar-Seasons datasets demonstrate that our model outperforms previous local features and gives competitive accuracy to advanced matchers but is about 2 and 3 times faster when using 2k and 4k keypoints, respectively. Code is available at https://***/feixue94/sfd2.

关键词： Low-level vision

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vision Transformers are Parameter-Efficient Audio-Visual Learners

Vision Transformers are Parameter-Efficient Audio-Visual Lea...

引用

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

作者： Lin, Yan-Bo Sung, Yi-Lin Lei, Jie Bansal, Mohit Bertasius, Gedas UNC Chapel Hill Dept Comp Sci Chapel Hill NC 27514 USA

ISBN: (纸本)9798350301298

vision transformers (ViTs) have achieved impressive results on various computer vision tasks in the last several years. In this work, we study the capability of frozen ViTs, pretrained only on visual data, to generalize to audio-visual data without finetuning any of its original parameters. To do so, we propose a latent audio-visual hybrid (LAVISH) adapter that adapts pretrained ViTs to audio-visual tasks by injecting a small number of trainable parameters into every layer of a frozen ViT. To efficiently fuse visual and audio cues, our LAVISH adapter uses a small set of latent tokens, which form an attention bottleneck, thus, eliminating the quadratic cost of standard cross-attention. Compared to the existing modality-specific audio-visual methods, our approach achieves competitive or even better performance on various audio-visual tasks while using fewer tunable parameters and without relying on costly audio pretraining or external audio encoders. Our code is available at https://***/project_page/LAVISH/

关键词： Multi-modal learning

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Robust Single Image Reflection Removal Against Adversarial Attacks

Robust Single Image Reflection Removal Against Adversarial A...

引用

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

作者： Song, Zhenbo Zhang, Zhenyuan Zhang, Kaihao Luo, Wenhan Fan, Zhaoxin Ren, Wenqi Lu, Jianfeng Nanjing Univ Sci & Technol Nanjing Peoples R China Australian Natl Univ Canberra ACT Australia Sun Yat Sen Univ Shenzhen Campus Shenzhen Peoples R China Renmin Univ China Beijing Peoples R China

ISBN: (纸本)9798350301298

This paper addresses the problem of robust deep single-image reflection removal (SIRR) against adversarial attacks. Current deep learning based SIRR methods have shown significant performance degradation due to unnoticeable distortions and perturbations on input images. For a comprehensive robustness study, we first conduct diverse adversarial attacks specifically for the SIRR problem, i.e. towards different attacking targets and regions. Then we propose a robust SIRR model, which integrates the cross-scale attention module, the multi-scale fusion module, and the adversarial image discriminator. By exploiting the multi-scale mechanism, the model narrows the gap between features from clean and adversarial images. The image discriminator adaptively distinguishes clean or noisy inputs, and thus further gains reliable robustness. Extensive experiments on Nature, SIR2, and Real datasets demonstrate that our model remarkably improves the robustness of SIRR across disparate scenes.

关键词： Low-level vision

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