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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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Extending global-local view alignment for self-supervised learning with remote sensing imagery

Extending global-local view alignment for self-supervised le...

引用

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

作者： Wanyan, Xinye Seneviratne, Sachith Shen, Shuchang Kirley, Michael

ISBN: (纸本)9798350365474

Since large number of high-quality remote sensing images are readily accessible, exploiting the corpus of images with less manual annotation draws increasing attention. Self-supervised models acquire general feature representations by formulating a pretext task that generates pseudolabels for massive unlabeled data to provide supervision for training. While prior studies have explored multiple self-supervised learning techniques in remote sensing domain, pretext tasks based on local-global view alignment remain underexplored, despite achieving state-of-the-art results on natural imagery. Inspired by DINO [6], which employs an effective representation learning structure with knowledge distillation based on global-local view alignment, we formulate two pretext tasks for self-supervised learning on remote sensing imagery (SSLRS). Using these tasks, we explore the effectiveness of positive temporal contrast as well as multi-sized views on SSLRS. We extend DINO and propose DINO-MC which uses local views of various sized crops instead of a single fixed size in order to alleviate the limited variation in object size observed in remote sensing imagery. Our experiments demonstrate that even when pre-trained on only 10% of the dataset, DINO-MC performs on par or better than existing state-of-the-art SSLRS methods on multiple remote sensing tasks, while using less computational resources. All codes, models, and results are released at https://***/WennyXY/DINO-MC.

关键词： computer vision remote sensing imagery self-supervised learning

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PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow

PyramidFlow: High-Resolution Defect Contrastive Localization...

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

作者： Lei, Jiarui Hu, Xiaobo Wang, Yue Liu, Dong Zhejiang Univ State Key Lab Modern Opt Instrumentat Hangzhou Peoples R China ZJU Hangzhou Global Sci & Technol Innovat Ctr Hangzhou Peoples R China Jiaxing Key Lab Photon Sensing & Intelligent Imag Jiaxing Peoples R China Zhejiang Univ Jiaxing Res Inst Intelligent Opt & Photon Res Ctr Hangzhou Peoples R China

ISBN: (纸本)9798350301298

During industrial processing, unforeseen defects may arise in products due to uncontrollable factors. Although unsupervised methods have been successful in defect localization, the usual use of pre-trained models results in low-resolution outputs, which damages visual performance. To address this issue, we propose PyramidFlow, the first fully normalizing flow method without pre-trained models that enables high-resolution defect localization. Specifically, we propose a latent template-based defect contrastive localization paradigm to reduce intra-class variance, as the pre-trained models do. In addition, PyramidFlow utilizes pyramid-like normalizing flows for multi-scale fusing and volume normalization to help generalization. Our comprehensive studies on MVTecAD demonstrate the proposed method outperforms the comparable algorithms that do not use external priors, even achieving state-of-the-art performance in more challenging BTAD scenarios.

关键词： Low-level vision

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

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

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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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Vim4Path: Self-Supervised vision Mamba for Histopathology Images

Vim4Path: Self-Supervised Vision Mamba for Histopathology Im...

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

作者： Nasiri-Sarvi, Ali Vincent Quoc-Huy Trinh Rivaz, Hassan Hosseini, Mahdi S. Concordia Univ Dept Comp Sci & Software Engn CSSE Montreal PQ Canada Univ Montreal Inst Res Immunol & Canc Montreal PQ Canada Concordia Univ Dept Elect & Comp Engn ECE Montreal PQ Canada

ISBN: (纸本)9798350365474

Representation learning from Gigapixel Whole Slide Images (WSI) poses a significant challenge in computational pathology due to the complicated nature of tissue structures and the scarcity of labeled data. Multi-instance learning methods have addressed this challenge, leveraging image patches to classify slides utilizing pretrained models using Self-Supervised Learning (SSL) approaches. The performance of both SSL and MIL methods relies on the architecture of the feature encoder. This paper proposes leveraging the vision Mamba (Vim) architecture, inspired by state space models, within the DINO framework for representation learning in computational pathology. We evaluate the performance of Vim against vision Transformers (ViT) on the Camelyon16 dataset for both patch-level and slide-level classification. Our findings highlight Vim's enhanced performance compared to ViT, particularly at smaller scales, where Vim achieves an 8.21 increase in ROC AUC for models of similar size. An explainability analysis further highlights Vim's capabilities, which reveals that Vim uniquely emulates the pathologist workflow-unlike ViT. This alignment with human expert analysis highlights Vim's potential in practical diagnostic settings and contributes significantly to developing effective representation-learning algorithms in computational pathology. We release the codes and pretrained weights at https: //***/AtlasAnalyticsLab/Vim4Path.

关键词： Cancer Diagnosis Computational Pathology Self-Supervised Learning vision Mamba Whole Slide Image

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VILA: Learning Image Aesthetics from User Comments with vision-Language Pretraining

VILA: Learning Image Aesthetics from User Comments with Visi...

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

作者： Ke, Junjie Ye, Keren Yu, Jiahui Wu, Yonghui Milanfar, Peyman Yang, Feng Google Res New York NY 10019 USA

ISBN: (纸本)9798350301298

Assessing the aesthetics of an image is challenging, as it is influenced by multiple factors including composition, color, style, and high-level semantics. Existing image aesthetic assessment (IAA) methods primarily rely on human-labeled rating scores, which oversimplify the visual aesthetic information that humans perceive. Conversely, user comments offer more comprehensive information and are a more natural way to express human opinions and preferences regarding image aesthetics. In light of this, we propose learning image aesthetics from user comments, and exploring vision-language pretraining methods to learn multimodal aesthetic representations. Specifically, we pretrain an image-text encoder-decoder model with image-comment pairs, using contrastive and generative objectives to learn rich and generic aesthetic semantics without human labels. To efficiently adapt the pretrained model for downstream IAA tasks, we further propose a lightweight rank-based adapter that employs text as an anchor to learn the aesthetic ranking concept. Our results show that our pretrained aesthetic vision-language model outperforms prior works on image aesthetic captioning over the AVA-Captions dataset, and it has powerful zero-shot capability for aesthetic tasks such as zero-shot style classification and zero-shot IAA, surpassing many supervised baselines. With only minimal fine-tuning parameters using the proposed adapter module, our model achieves state-of-the-art IAA performance over the AVA dataset.(1)

关键词： Computational imaging

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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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HIER: Metric Learning Beyond Class Labels via Hierarchical Regularization

HIER: Metric Learning Beyond Class Labels via Hierarchical R...

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

作者： Kim, Sungyeon Jeong, Boseung Kwak, Suha POSTECH Dept CSE Pohang South Korea POSTECH Grad Sch AI Pohang South Korea

ISBN: (纸本)9798350301298

Supervision for metric learning has long been given in the form of equivalence between human-labeled classes. Although this type of supervision has been a basis of metric learning for decades, we argue that it hinders further advances in the field. In this regard, we propose a new regularization method, dubbed HIER, to discover the latent semantic hierarchy of training data, and to deploy the hierarchy to provide richer and more fine-grained supervision than inter-class separability induced by common metric learning losses. HIER achieves this goal with no annotation for the semantic hierarchy but by learning hierarchical proxies in hyperbolic spaces. The hierarchical proxies are learnable parameters, and each of them is trained to serve as an ancestor of a group of data or other proxies to approximate the semantic hierarchy among them. HIER deals with the proxies along with data in hyperbolic space since the geometric properties of the space are well-suited to represent their hierarchical structure. The efficacy of HIER is evaluated on four standard benchmarks, where it consistently improved the performance of conventional methods when integrated with them, and consequently achieved the best records, surpassing even the existing hyperbolic metric learning technique, in almost all settings.

关键词： detection recognition: Categorization retrieval

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Modality-invariant Visual Odometry for Embodied vision

Modality-invariant Visual Odometry for Embodied Vision

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

作者： Memmel, Marius Bachmann, Roman Zamir, Amir Univ Washington Seattle WA 98195 USA Swiss Fed Inst Technol EPFL Lausanne Switzerland

ISBN: (纸本)9798350301298

Effectively localizing an agent in a realistic, noisy setting is crucial for many embodied vision tasks. Visual Odometry (VO) is a practical substitute for unreliable GPS and compass sensors, especially in indoor environments. While SLAM-based methods show a solid performance without large data requirements, they are less flexible and robust w.r.t. to noise and changes in the sensor suite compared to learning-based approaches. Recent deep VO models, however, limit themselves to a fixed set of input modalities, e.g., RGB and depth, while training on millions of samples. When sensors fail, sensor suites change, or modalities are intentionally looped out due to available resources, e.g., power consumption, the models fail catastrophically. Furthermore, training these models from scratch is even more expensive without simulator access or suitable existing models that can be fine-tuned. While such scenarios get mostly ignored in simulation, they commonly hinder a model's reusability in real-world applications. We propose a Transformer-based modality-invariant VO approach that can deal with diverse or changing sensor suites of navigation agents. Our model outperforms previous methods while training on only a fraction of the data. We hope this method opens the door to a broader range of real-world applications that can benefit from flexible and learned VO models.

关键词： Embodied vision: Active agents simulation

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Recursive Joint Cross-Modal Attention for Multimodal Fusion in Dimensional Emotion recognition

Recursive Joint Cross-Modal Attention for Multimodal Fusion ...

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

作者： Praveen, R. Gnana Alam, Jahangir Comp Res Inst Montreal CRIM Montreal PQ Canada

ISBN: (纸本)9798350365474

Though multimodal emotion recognition has achieved significant progress over recent years, the potential of rich synergic relationships across the modalities is not fully exploited. In this paper, we introduce Recursive Joint Cross-Modal Attention (RJCMA) to effectively capture both intra- and inter-modal relationships across audio, visual, and text modalities for dimensional emotion recognition. In particular, we compute the attention weights based on cross-correlation between the joint audio-visual-text feature representations and the feature representations of individual modalities to simultaneously capture intra- and intermodal relationships across the modalities. The attended features of the individual modalities are again fed as input to the fusion model in a recursive mechanism to obtain more refined feature representations. We have also explored Temporal Convolutional Networks (TCNs) to improve the temporal modeling of the feature representations of individual modalities. Extensive experiments are conducted to evaluate the performance of the proposed fusion model on the challenging Affwild2 dataset. By effectively capturing the synergic intra- and inter-modal relationships across audio, visual, and text modalities, the proposed fusion model achieves a Concordance Correlation Coefficient (CCC) of 0.585 (0.542) and 0.674 (0.619) for valence and arousal, respectively, on the validation set (test set). This shows a significant improvement over the baseline of 0.240 (0.211) and 0.200 (0.191) for valence and arousal, respectively, in the validation set (test set), achieving second place in the valence-arousal challenge of the 6th Affective Behavior Analysis in-the-Wild (ABAW) competition. The code is available on GitHub: https: //***/praveena2j/RJCMA.

关键词： Concordance Correlation Coefficient Cross-Attention Dimensional Emotion recognition Joint Respresentation Multimodal Fusion Recursive Fusion

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