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检索条件"任意字段=2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020"

共 11281 条记录，以下是311-320 订阅

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Part-aware Unified Representation of Language and Skeleton for Zero-shot Action recognition

Part-aware Unified Representation of Language and Skeleton f...

引用

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

作者： Zhu, Anqi Ke, Qiuhong Gong, Mingming Bailey, James Univ Melbourne Melbourne Vic Australia Monash Univ Parkville Vic 3052 Australia Wellington Rd Clayton Vic 3800 Australia

ISBN: (纸本)9798350353006

While remarkable progress has been made on supervised skeleton-based action recognition, the challenge of zero-shot recognition remains relatively unexplored. In this paper, we argue that relying solely on aligning label-level semantics and global skeleton features is insufficient to effectively transfer locally consistent visual knowledge from seen to unseen classes. To address this limitation, we introduce Part-aware Unified Representation between Language and Skeleton (PURLS) to explore visual-semantic alignment at both local and global scales. PURLS introduces a new prompting module and a novel partitioning module to generate aligned textual and visual representations across different levels. The former leverages a pre-trained GPT-3 to infer refined descriptions of the global and local (body-part-based and temporal-interval-based) movements from the original action labels. The latter employs an adaptive sampling strategy to group visual features from all body joint movements that are semantically relevant to a given description. Our approach is evaluated on various skeleton/language backbones and three large-scale datasets, i.e., NTU-RGB+D 60, NTU-RGB+D 120, and a newly curated dataset Kinetics-skeleton 200. The results showcase the universality and superior performance of PURLS, surpassing prior skeleton-based solutions and standard baselines from other domains. The source codes can be accessed at https://***/azzh1/PURLS.

关键词： action recognition computer vision contrastive learning large language model representation learning skeleton action recognition zero-shot learning

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RoMa: Robust Dense Feature Matching

RoMa: Robust Dense Feature Matching

引用

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

作者： Edstedt, Johan Sun, Qiyu Bokman, Georg Wadenback, Marten Felsberg, Michael Linkoping Univ Linkoping Sweden East China Univ Sci & Technol Shanghai Peoples R China Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9798350353006

Feature matching is an important computer vision task that involves estimating correspondences between two images of a 3D scene, and dense methods estimate all such correspondences. The aim is to learn a robust model, i.e., a model able to match under challenging real-world changes. In this work, we propose such a model, leveraging frozen pretrained features from the foundation model DINOv2. Although these features are significantly more robust than local features trained from scratch, they are inherently coarse. We therefore combine them with specialized ConvNet fine features, creating a precisely localizable feature pyramid. To further improve robustness, we propose a tailored transformer match decoder that predicts anchor probabilities, which enables it to express multimodality. Finally, we propose an improved loss formulation through regression-by-classification with subsequent robust regression. We conduct a comprehensive set of experiments that show that our method, RoMa, achieves significant gains, setting a new state-of-the-art. In particular, we achieve a 36% improvement on the extremely challenging WxBS benchmark. Code is provided at ***/Parskatt/RoMa.

关键词： 3D vision dense feature matching dense matching feature matching geometry estimation image matching two-view geometry

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GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View Synthesis

GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting...

引用

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

作者： Zheng, Shunyuan Zhou, Boyao Shao, Ruizhi Liu, Boning Zhang, Shengping Nie, Liqiang Liu, Yebin Harbin Inst Technol Harbin Peoples R China Tsinghua Univ Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350353006

We present a new approach, termed GPS-Gaussian, for synthesizing novel views of a character in a real-time manner. The proposed method enables 2K-resolution rendering under a sparse-view camera setting. Unlike the original Gaussian Splatting or neural implicit rendering methods that necessitate per-subject optimizations, we introduce Gaussian parameter maps defined on the source views and regress directly Gaussian Splatting properties for instant novel view synthesis without any fine-tuning or optimization. To this end, we train our Gaussian parameter regression module on a large amount of human scan data, jointly with a depth estimation module to lift 2D parameter maps to 3D space. The proposed framework is fully differentiable and experiments on several datasets demonstrate that our method outperforms state-of-the-art methods while achieving an exceeding rendering speed. The code is available at https://***/aipixel/GPS-Gaussian.

关键词： Rendering (computer graphics)

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Robust Emotion recognition in Context Debiasing

Robust Emotion Recognition in Context Debiasing

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

作者： Yang, Dingkang Yang, Kun Li, Mingcheng Wang, Shunli Wang, Shuaibing Zhang, Lihua Fudan Univ Acad Engn & Technol Shanghai Peoples R China Cognit & Intelligent Technol Lab CIT Lab Beijing Peoples R China Jilin Prov Key Lab Intelligence Sci & Engn Changchun Peoples R China Minist Educ Engn Res Ctr AI & Robot Shanghai Peoples R China

ISBN: (纸本)9798350353006

Context-aware emotion recognition (CAER) has recently boosted the practical applications of affective computing techniques in unconstrained environments. Mainstream CAER methods invariably extract ensemble representations from diverse contexts and subject-centred characteristics to perceive the target person's emotional state. Despite advancements, the biggest challenge remains due to context bias interference. The harmful bias forces the models to rely on spurious correlations between background contexts and emotion labels in likelihood estimation, causing severe performance bottlenecks and confounding valuable context priors. In this paper, we propose a counterfactual emotion inference (CLEF) framework to address the above issue. Specifically, we first formulate a generalized causal graph to decouple the causal relationships among the variables in CAER. Following the causal graph, CLEF introduces a non-invasive context branch to capture the adverse direct effect caused by the context bias. During the inference, we eliminate the direct context effect from the total causal effect by comparing factual and counterfactual outcomes, resulting in bias mitigation and robust prediction. As a model-agnostic framework, CLEF can be readily integrated into existing methods, bringing consistent performance gains.

关键词： Counterfactual inference Emotion recognition

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Beyond Image Super-Resolution for Image recognition with Task-Driven Perceptual Loss

Beyond Image Super-Resolution for Image Recognition with Tas...

引用

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

作者： Kim, Jaeha Oh, Junghun Lee, Kyoung Mu Seoul Natl Univ Dept ECE Seoul South Korea Seoul Natl Univ ASRI Seoul South Korea Seoul Natl Univ IPAI Seoul South Korea

ISBN: (纸本)9798350353013;9798350353006

In real-world scenarios, image recognition tasks, such as semantic segmentation and object detection, often pose greater challenges due to the lack of information available within low-resolution (LR) content. Image super-resolution (SR) is one of the promising solutions for addressing the challenges. However, due to the ill-posed property of SR, it is challenging for typical SR methods to restore task-relevant high-frequency contents, which may dilute the advantage of utilizing the SR method. Therefore, in this paper, we propose Super-Resolution for Image recognition (SR4IR) that effectively guides the generation of SR images beneficial to achieving satisfactory image recognition performance when processing LR images. The critical component of our SR4IR is the task-driven perceptual (TDP) loss that enables the SR network to acquire task-specific knowledge from a network tailored for a specific task. Moreover, we propose a cross-quality patch mix and an alternate training framework that significantly enhances the efficacy of the TDP loss by addressing potential problems when employing the TDP loss. Through extensive experiments, we demonstrate that our SR4IR achieves outstanding task performance by generating SR images useful for a specific image recognition task, including semantic segmentation, object detection, and image classification. The implementation code is available at https://***/JaehaKim97/SR4IR.

关键词： Low-level vision Perceptual loss Super-resolution Task-aware restoration

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Atom-Level Optical Chemical Structure recognition with Limited Supervision

Atom-Level Optical Chemical Structure Recognition with Limit...

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

作者： Oldenhof, Martijn De Brouwer, Edward Arany, Adam Moreau, Yves Katholieke Univ Leuven ESAT STADIUS Leuven Belgium Yale Univ New Haven CT USA

ISBN: (纸本)9798350353006

Identifying the chemical structure from a graphical representation, or image, of a molecule is a challenging pattern recognition task that would greatly benefit drug development. Yet, existing methods for chemical structure recognition do not typically generalize well, and show diminished effectiveness when confronted with domains where data is sparse, or costly to generate, such as hand-drawn molecule images. To address this limitation, we propose a new chemical structure recognition tool that delivers state-of-the-art performance and can adapt to new domains with a limited number of data samples and supervision. Unlike previous approaches, our method provides atom-level localization, and can therefore segment the image into the different atoms and bonds. Our model is the first model to perform OCSR with atom-level entity detection with only SMILES supervision. Through rigorous and extensive benchmarking, we demonstrate the preeminence of our chemical structure recognition approach in terms of data efficiency, accuracy, and atom-level entity prediction.

关键词： graph recognition object detection OCSR weak supervision

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Efficient Skeleton-Based Action recognition for Real-Time Embedded Systems

Efficient Skeleton-Based Action Recognition for Real-Time Em...

引用

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

作者： Noor, Nadhira Jametoni, Fabianaugie Kim, Jinbeom Hong, Hyunsu Park, In Kyu Inha Univ Dept Elect & Comp Engn Incheon 22212 South Korea Finedigital Inc Seongnam Si 13496 Gyeonggi Do South Korea

ISBN: (纸本)9798350365474

Action recognition is vital for various real-world applications, yet its implementation on embedded systems or edge devices faces challenges due to limited computing and memory resources. Our goal is to facilitate lightweight action recognition on embedded systems by utilizing skeleton-based techniques, which naturally require less computing and memory resources. To achieve this, we propose innovative methodologies and optimizations tailored for embedded deployment, including post-training quantization, optimized model architectures, and efficient resource utilization. By enabling real-time and lightweight action recognition on resource-constrained embedded systems, our research opens up new possibilities for applications in areas like autonomous surveillance, driving, and indoor safety monitoring.

关键词： Action recognition convolutional neural network embedded system skeleton-based

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Pre-trained vision and Language Transformers Are Few-Shot Incremental Learners

Pre-trained Vision and Language Transformers Are Few-Shot In...

引用

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

作者： Park, Keon-Hee Song, Kyungwoo Park, Gyeong-Moon Kyung Hee Univ Seoul South Korea Yonsei Univ Seoul South Korea

ISBN: (纸本)9798350353006

Few-Shot Class Incremental Learning (FSCIL) is a task that requires a model to learn new classes incrementally without forgetting when only a few samples for each class are given. FSCIL encounters two significant challenges: catastrophic forgetting and overfitting, and these challenges have driven prior studies to primarily rely on shallow models, such as ResNet-18. Even though their limited capacity can mitigate both forgetting and overfitting issues, it leads to inadequate knowledge transfer during few-shot incremental sessions. In this paper, we argue that large models such as vision and language transformers pre-trained on large datasets can be excellent few-shot incremental learners. To this end, we propose a novel FSCIL framework called PriViLege, Pre-trained vision and Language transformers with prompting functions and knowledge distillation. Our framework effectively addresses the challenges of catastrophic forgetting and overfitting in large models through new pre-trained knowledge tuning (PKT) and two losses: entropy-based divergence loss and semantic knowledge distillation loss. Experimental results show that the proposed PriViLege significantly outperforms the existing state-of-the-art methods with a large margin, e.g., +9.38% in CUB200, +20.58% in CIFAR-100, and +13.36% in miniImageNet. Our implementation code is available at https://***/KHU-AGI/PriViLege.

关键词： Few-shot learning Incremental learning Parameter Efficient Tuning

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RMT: Retentive Networks Meet vision Transformers

RMT: Retentive Networks Meet Vision Transformers

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

作者： Fan, Qihang Huang, Huaibo Chen, Mingrui Liu, Hongmin He, Ran Chinese Acad Sci Inst Automat MAIS & CRIPAC Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Univ Sci & Technol Beijing Beijing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

vision Transformer (ViT) has gained increasing attention in the computer vision community in recent years. However, the core component of ViT, Self-Attention, lacks explicit spatial priors and bears a quadratic computational complexity, thereby constraining the applicability of ViT. To alleviate these issues, we draw inspiration from the recent Retentive Network (RetNet) in the field of NLP, and propose RMT, a strong vision backbone with explicit spatial prior for general purposes. Specifically, we extend the RetNet's temporal decay mechanism to the spatial domain, and propose a spatial decay matrix based on the Manhattan distance to introduce the explicit spatial prior to Self-Attention. Additionally, an attention decomposition form that adeptly adapts to explicit spatial prior is proposed, aiming to reduce the computational burden of modeling global information without disrupting the spatial decay matrix. Based on the spatial decay matrix and the attention decomposition form, we can flexibly integrate explicit spatial prior into the vision backbone with linear complexity. Extensive experiments demonstrate that RMT exhibits exceptional performance across various vision tasks. Specifically, without extra training data, RMT achieves 84.8% and 86.1% top-1 acc on ImageNet-1k with 27M/4.5GFLOPs and 96M/18.2GFLOPs. For downstream tasks, RMT achieves 54.5 box AP and 47.2 mask AP on the COCO detection task, and 52.8 mIoU on the ADE20K se-mantic segmentation task.

关键词： vision Transformer

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One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained vision-Language Models

One Prompt Word is Enough to Boost Adversarial Robustness fo...

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

作者： Lin, L. Guan, Haoyan Qiu, Jianing Spratling, Michael Kings Coll London London England Imperial Coll London London England

ISBN: (纸本)9798350353006

Large pre-trained vision-Language Models (VLMs) like CLIP, despite having remarkable generalization ability, are highly vulnerable to adversarial examples. This work studies the adversarial robustness of VLMs from the novel perspective of the text prompt instead of the extensively studied model weights (frozen in this work). We first show that the effectiveness of both adversarial attack and defense are sensitive to the used text prompt. Inspired by this, we propose a method to improve resilience to adversarial attacks by learning a robust text prompt for VLMs. The proposed method, named Adversarial Prompt Tuning (APT), is effective while being both computationally and data efficient. Extensive experiments are conducted across 15 datasets and 4 data sparsity schemes (from 1-shot to full training data settings) to show APT's superiority over hand-engineered prompts and other state-of-the-art adaption methods. APT demonstrated excellent abilities in terms of the in-distribution performance and the generalization under input distribution shift and across datasets. Surprisingly, by simply adding one learned word to the prompts, APT can significantly boost the accuracy and robustness ((sic)=4/255 ) over the hand-engineered prompts by +13% and +8.5% on average respectively. The improvement further increases, in our most effective setting, to +26.4% for accuracy and +16.7% for robustness. Code is available at https://***/TreeLLi/APT.

关键词： adversarial examples adversarial robustness CLIP text prompting vision-language models VLMs

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