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PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification

PIP-Net: Patch-Based Intuitive Prototypes for Interpretable ...

引用

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

作者： Nauta, Meike Schloetterer, Joerg van Keulen, Maurice Seifert, Christin Univ Twente Enschede Netherlands Univ Duisburg Essen Essen Germany

ISBN: (纸本)9798350301298

Interpretable methods based on prototypical patches recognize various components in an image in order to explain their reasoning to humans. However, existing prototype-based methods can learn prototypes that are not in line with human visual perception, i.e., the same prototype can refer to different concepts in the real world, making interpretation not intuitive. Driven by the principle of explainability-bydesign, we introduce PIP-Net (Patch-based Intuitive Prototypes Network): an interpretable image classification model that learns prototypical parts in a self-supervised fashion which correlate better with human vision. PIP-Net can be interpreted as a sparse scoring sheet where the presence of a prototypical part in an image adds evidence for a class. The model can also abstain from a decision for out-of-distribution data by saying "I haven't seen this before". We only use image-level labels and do not rely on any part annotations. PIP-Net is globally interpretable since the set of learned prototypes shows the entire reasoning of the model. A smaller local explanation locates the relevant prototypes in one image. We show that our prototypes correlate with ground-truth object parts, indicating that PIP-Net closes the "semantic gap" between latent space and pixel space. Hence, our PIP-Net with interpretable prototypes enables users to interpret the decision making process in an intuitive, faithful and semantically meaningful way. Code is available at https://***/M- Nauta/PIPNet.

关键词： Explainable computer vision

来源：评论

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Neural Koopman Pooling: Control-Inspired Temporal Dynamics Encoding for Skeleton-Based Action recognition

Neural Koopman Pooling: Control-Inspired Temporal Dynamics E...

引用

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

作者： Wang, Xinghan Xu, Xin Mu, Yadong Peking Univ Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Skeleton-based human action recognition is becoming increasingly important in a variety of fields. Most existing works train a CNN or GCN based backbone to extract spatial-temporal features, and use temporal average/max pooling to aggregate the information. However, these pooling methods fail to capture high-order dynamics information. To address the problem, we propose a plug-and-play module called Koopman pooling, which is a parameterized high-order pooling technique based on Koopman theory. The Koopman operator linearizes a non-linear dynamics system, thus providing a way to represent the complex system through the dynamics matrix, which can be used for classification. We also propose an eigenvalue normalization method to encourage the learned dynamics to be non-decaying and stable. Besides, we also show that our Koopman pooling framework can be easily extended to one-shot action recognition when combined with Dynamic Mode Decomposition. The proposed method is evaluated on three benchmark datasets, namely NTU RGB+D 60, 120 and NW-UCLA. Our experiments clearly demonstrate that Koopman pooling significantly improves the performance under both full-dataset and one-shot settings.

关键词： detection recognition: Categorization retrieval

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Multi-Label Compound Expression recognition: C-EXPR Database & Network

Multi-Label Compound Expression Recognition: C-EXPR Database...

引用

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

作者： Kollias, Dimitrios Queen Mary Univ London London England

ISBN: (纸本)9798350301298

Research in automatic analysis of facial expressions mainly focuses on recognising the seven basic ones. However, compound expressions are more diverse and represent the complexity and subtlety of our daily affective displays more accurately. Limited research has been conducted for compound expression recognition (CER), because only a few databases exist, which are small, lab controlled, im-balanced and static. In this paper we present an in-thewild A/V database, C-EXPR-DB, consisting of 400 videos of 200K frames, annotated in terms of 13 compound expressions, valence-arousal emotion descriptors, action units, speech, facial landmarks and attributes. We also propose C-EXPR-NET, a multi-task learning (MTL) method for CER and AU detection (AU-D);the latter task is introduced to enhance CER performance. For AU-D we incorporate AU semantic description along with visual information. For CER we use a multi-label formulation and the KL-divergence loss. We also propose a distribution matching loss for coupling CER and AU-D tasks to boost their performance and alleviate negative transfer (i.e., when MT model's performance is worse than that of at least one single-task model). An extensive experimental study has been conducted illustrating the excellent performance of C-EXPR-NET, validating the theoretical claims. Finally, C-EXPR-NET is shown to effectively generalize its knowledge in new emotion recognition contexts, in a zero-shot manner.

关键词： body gesture Humans: Face movement pose

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Learning Generative Structure Prior for Blind Text Image Super-resolution

Learning Generative Structure Prior for Blind Text Image Sup...

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

作者： Li, Xiaoming Zuo, Wangmeng Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore Harbin Inst Technol Harbin Peoples R China

ISBN: (纸本)9798350301298

Blind text image super-resolution (SR) is challenging as one needs to cope with diverse font styles and unknown degradation. To address the problem, existing methods perform character recognition in parallel to regularize the SR task, either through a loss constraint or intermediate feature condition. Nonetheless, the high-level prior could still fail when encountering severe degradation. The problem is further compounded given characters of complex structures, e.g., Chinese characters that combine multiple pictographic or ideographic symbols into a single character. In this work, we present a novel prior that focuses more on the character structure. In particular, we learn to encapsulate rich and diverse structures in a StyleGAN and exploit such generative structure priors for restoration. To restrict the generative space of StyleGAN so that it obeys the structure of characters yet remains flexible in handling different font styles, we store the discrete features for each character in a codebook. The code subsequently drives the StyleGAN to generate high-resolution structural details to aid text SR. Compared to priors based on character recognition, the proposed structure prior exerts stronger character-specific guidance to restore faithful and precise strokes of a designated character. Extensive experiments on synthetic and real datasets demonstrate the compelling performance of the proposed generative structure prior in facilitating robust text SR. Our code is available at https://***/csxmli2016/MARCONet.

关键词： Low-level vision

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MIDAS: Mixing Ambiguous Data with Soft Labels for Dynamic Facial Expression recognition

MIDAS: Mixing Ambiguous Data with Soft Labels for Dynamic Fa...

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

作者： Kawamura, Ryosuke Hayashi, Hideaki Takemura, Noriko Nagahara, Hajime Fujitsu Res Amer Inc Richardson TX 75082 USA Osaka Univ Osaka Japan Kyushu Inst Technol Kitakyushu Japan

ISBN: (纸本)9798350318920;9798350318937

Dynamic facial expression recognition (DFER) is an important task in the field of computer vision. To apply automatic DFER in practice, it is necessary to accurately recognize ambiguous facial expressions, which often appear in data in the wild. In this paper, we propose MIDAS, a data augmentation method for DFER, which augments ambiguous facial expression data with soft labels consisting of probabilities for multiple emotion classes. In MIDAS, the training data are augmented by convexly combining pairs of video frames and their corresponding emotion class labels, which can also be regarded as an extension of mixup to soft-labeled video data. This simple extension is remarkably effective in DFER with ambiguous facial expression data. To evaluate MIDAS, we conducted experiments on the DFEW dataset. The results demonstrate that the model trained on the data augmented by MIDAS outperforms the existing state-of-the-art method trained on the original dataset.

关键词： Algorithms Algorithms Biometrics body pose face gesture Video recognition and understanding

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DeCo : Decomposition and Reconstruction for Compositional Temporal Grounding via Coarse-to-Fine Contrastive Ranking

DeCo : Decomposition and Reconstruction for Compositional Te...

引用

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

作者： Yang, Lijin Kong, Quan Yang, Hsuan-Kung Kehl, Wadim Sato, Yoichi Kobori, Norimasa Univ Tokyo Tokyo Japan Woven Toyota Tokyo Japan

ISBN: (纸本)9798350301298

Understanding dense action in videos is a fundamental challenge towards the generalization of vision models. Several works show that compositionality is key to achieving generalization by combining known primitive elements, especially for handling novel composited structures. Compositional temporal grounding is the task of localizing dense action by using known words combined in novel ways in the form of novel query sentences for the actual grounding. In recent works, composition is assumed to be learned from pairs of whole videos and language embeddings through large scale self-supervised pre-training. Alternatively, one can process the video and language into word-level primitive elements, and then only learn fine-grained semantic correspondences. Both approaches do not consider the granularity of the compositions, where different query granularity corresponds to different video segments. Therefore, a good compositional representation should be sensitive to different video and query granularity. We propose a method to learn a coarse-to-fine compositional representation by decomposing the original query sentence into different granular levels, and then learning the correct correspondences between the video and recombined queries through a contrastive ranking constraint. Additionally, we run temporal boundary prediction in a coarse-to-fine manner for precise grounding boundary detection. Experiments are performed on two datasets, Charades-CG and ActivityNet-CG, showing the superior compositional generalizability of our approach.

关键词： and reasoning language vision

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Improving Table Structure recognition with Visual-Alignment Sequential Coordinate Modeling

Improving Table Structure Recognition with Visual-Alignment ...

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

作者： Huang, Yongshuai Lu, Ning Chen, Dapeng Li, Yibo Xie, Zecheng Zhu, Shenggao Gao, Liangcai Peng, Wei Huawei Technol Ltd Shenzhen Peoples R China Peking Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Table structure recognition aims to extract the logical and physical structure of unstructured table images into a machine-readable format. The latest end-to-end image-to-text approaches simultaneously predict the two structures by two decoders, where the prediction of the physical structure (the bounding boxes of the cells) is based on the representation of the logical structure. However, the previous methods struggle with imprecise bounding boxes as the logical representation lacks local visual information. To address this issue, we propose an end-to-end sequential modeling framework for table structure recognition called VAST. It contains a novel coordinate sequence decoder triggered by the representation of the non-empty cell from the logical structure decoder. In the coordinate sequence decoder, we model the bounding box coordinates as a language sequence, where the left, top, right and bottom coordinates are decoded sequentially to leverage the inter-coordinate dependency. Furthermore, we propose an auxiliary visual-alignment loss to enforce the logical representation of the non-empty cells to contain more local visual details, which helps produce better cell bounding boxes. Extensive experiments demonstrate that our proposed method can achieve state-of-the-art results in both logical and physical structure recognition. The ablation study also validates that the proposed coordinate sequence decoder and the visual-alignment loss are the keys to the success of our method.

关键词： Document analysis and understanding

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Generic-to-Specific Distillation of Masked Autoencoders

Generic-to-Specific Distillation of Masked Autoencoders

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

作者： Huang, Wei Peng, Zhiliang Dong, Li Wei, Furu Jiao, Jianbin Ye, Qixiang Univ Chinese Acad Sci Beijing Peoples R China Microsoft Research Redmond WA USA

ISBN: (纸本)9798350301298

Large vision Transformers (ViTs) driven by self-supervised pre-training mechanisms achieved unprecedented progress. Lightweight ViT models limited by the model capacity, however, benefit little from those pre-training mechanisms. Knowledge distillation defines a paradigm to transfer representations from large (teacher) models to small (student) ones. However, the conventional single-stage distillation easily gets stuck on task-specific transfer, failing to retain the task-agnostic knowledge crucial for model generalization. In this study, we propose generic-to-specific distillation (G2SD), to tap the potential of small ViT models under the supervision of large models pre-trained by masked autoencoders. In generic distillation, decoder of the small model is encouraged to align feature predictions with hidden representations of the large model, so that task-agnostic knowledge can be transferred. In specific distillation, predictions of the small model are constrained to be consistent with those of the large model, to transfer task-specific features which guarantee task performance. With G2SD, the vanilla ViT-Small model respectively achieves 98.7%, 98.1% and 99.3% the performance of its teacher (ViT-Base) for image classification, object detection, and semantic segmentation, setting a solid baseline for two-stage vision distillation. Code will be available at https://***/pengzhiliang/G2SD.

关键词： Efficient and scalable vision

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Meta-Personalizing vision-Language Models to Find Named Instances in Video

Meta-Personalizing Vision-Language Models to Find Named Inst...

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

作者： Yeh, Chun-Hsiao Russell, Bryan Sivic, Josef Heilbron, Fabian Caba Jenni, Simon Univ Calif Berkeley Berkeley CA 94720 USA Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague Czech Republic Adobe Res San Francisco CA 94107 USA

ISBN: (纸本)9798350301298

Large-scale vision-language models (VLM) have shown impressive results for language-guided search applications. While these models allow category-level queries, they currently struggle with personalized searches for moments in a video where a specific object instance such as "My dog Biscuit" appears. We present the following three contributions to address this problem. First, we describe a method to meta-personalize a pre-trained VLM, i.e., learning how to learn to personalize a VLM at test time to search in video. Our method extends the VLM's token vocabulary by learning novel word embeddings specific to each instance. To capture only instance-specific features, we represent each instance embedding as a combination of shared and learned global category features. Second, we propose to learn such personalization without explicit human supervision. Our approach automatically identifies moments of named visual instances in video using transcripts and vision-language similarity in the VLM's embedding space. Finally, we introduce This-Is-My, a personal video instance retrieval benchmark. We evaluate our approach on This-Is-My and Deep-Fashion2 and show that we obtain a 15% relative improvement over the state of the art on the latter dataset.

关键词： language reasoning vision

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Matching Is Not Enough: A Two-Stage Framework for Category-Agnostic Pose Estimation

Matching Is Not Enough: A Two-Stage Framework for Category-A...

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

作者： Shi, Min Huang, Zihao Ma, Xianzheng Hu, Xiaowei Cao, Zhiguo Huazhong Univ Sci & Technol Sch AIA Wuhan Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Category-agnostic pose estimation (CAPE) aims to predict keypoints for arbitrary categories given support images with keypoint annotations. Existing approaches match the keypoints across the image for localization. However, such a one-stage matching paradigm shows inferior accuracy: the prediction heavily relies on the matching results, which can be noisy due to the open set nature in CAPE. For example, two mirror-symmetric keypoints (e.g., left and right eyes) in the query image can both trigger high similarity on certain support keypoints (eyes), which leads to duplicated or opposite predictions. To calibrate the inaccurate matching results, we introduce a two-stage framework, where matched keypoints from the first stage are viewed as similarity-aware position proposals. Then, the model learns to fetch relevant features to correct the initial proposals in the second stage. We instantiate the framework with a transformer model tailored for CAPE. The transformer encoder incorporates specific designs to improve the representation and similarity modeling in the first matching stage. In the second stage, similarity-aware proposals are packed as queries in the decoder for refinement via cross-attention. Our method surpasses the previous best approach by large margins on CAPE benchmark MP-100 on both accuracy and efficiency. Code available at ***/flyinglynx/CapeFormer

关键词： detection recognition: Categorization retrieval

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