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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2000"

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Context-aware Biaffine Localizing Network for Temporal Sentence Grounding

Context-aware Biaffine Localizing Network for Temporal Sente...

引用

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

作者： Liu, Daizong Qu, Xiaoye Dong, Jianfeng Zhou, Pan Cheng, Yu Wei, Wei Xu, Zichuan Xie, Yulai Huazhong Univ Sci & Technol Wuhan Hubei Peoples R China Huawei Cloud Shenzhen Peoples R China Zhejiang Gongshang Univ Hangzhou Zhejiang Peoples R China Microsoft AI Redmond WA USA Dalian Univ Technol Dalian Liaoning Peoples R China

ISBN: (纸本)9781665445092

This paper addresses the problem of temporal sentence grounding (TSG), which aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence query. Previous works either compare pre-defined candidate segments with the query and select the best one by ranking, or directly regress the boundary timestamps of the target segment. In this paper, we propose a novel localization framework that scores all pairs of start and end indices within the video simultaneously with a biaffine mechanism. In particular, we present a Contextaware Biaffine Localizing Network (CBLN) which incorporates both local and global contexts into features of each start/end position for biaffine-based localization. The local contexts from the adjacent frames help distinguish the visually similar appearance, and the global contexts from the entire video contribute to reasoning the temporal relation. Besides, we also develop a multi-modal self-attention module to provide fine-grained query-guided video representation for this biaffine strategy. Extensive experiments show that our CBLN significantly outperforms state-of-thearts on three public datasets (ActivityNet Captions, TACoS, and Charades-STA), demonstrating the effectiveness of the proposed localization framework. The code is available at https://***/liudaizong/CBLN.

关键词： Location awareness computer vision Codes Grounding Cognition pattern recognition Task analysis

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recognition of Symmetry Structure by Use of Gestalt Algebra

Recognition of Symmetry Structure by Use of Gestalt Algebra

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Michaelsen, Eckart Muench, David Arens, Michael Fraunhofer IOSB D-76275 Ettlingen Germany

ISBN: (纸本)9780769549903

While most approaches to symmetry detection in machine vision try to explain the gray-values or colors of the pixels, Gestalt algebra has no room for such measurement data. The entities (i.e. Gestalten) are only defined with respect to each other. They form a generic hierarchy, and live in a continuous domain without any pixel raster. There is also no constraint forcing them to completely fill an image, or prohibiting overlap. Yet, when used as a tool for symmetry recognition, the algebra must be somehow connected to the given data. In this paper this is done only on the primitive level using the well-known SIFT feature detector. From a set of such SIFT-based Gestalten follows a combinatorial set of higher-order symmetric Gestalten by constructing all possible terms using the operations of the algebra. The Gestalt domain contains a quality or assessment dimension. Taking the best Gestalten with respect to this attribute and clustering them yields the output for this competition participation.

关键词： algebra computer vision feature extraction object detection object recognition

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Pixel-Guided Dual-Branch Attention Network for Joint Image Deblurring and Super-Resolution

Pixel-Guided Dual-Branch Attention Network for Joint Image D...

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

作者： Xi, Si Wei, Jia Zhang, Weidong Netease Games AI Lab Hangzhou Peoples R China

ISBN: (纸本)9781665448994

Image deblurring and super-resolution (SR) are computer vision tasks aiming to restore image detail and spatial scale, respectively. Besides, only a few recent works of literature contribute to this task, as conventional methods deal with SR or deblurring separately. We focus on designing a novel Pixel-Guided dual-branch attention network (PDAN) that handles both tasks jointly to address this issue. Then, we propose a novel loss function better focus on large and medium range errors. Extensive experiments demonstrated that the proposed PDAN with the novel loss function not only generates remarkably clear HR images and achieves compelling results for joint image deblurring and SR tasks. In addition, our method achieves second place in NTIRE 2021 Challenge on track 1 of the Image Deblurring Challenge.

关键词： Training computer vision conferences Superresolution Feature extraction Image restoration pattern recognition

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Rethinking computer-aided Tuberculosis Diagnosis

Rethinking Computer-aided Tuberculosis Diagnosis

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

作者： Liu, Yun Wu, Yu-Huan Ban, Yunfeng Wang, Huifang Cheng, Ming-Ming Nankai Univ Coll Comp Sci TKLNDST Tianjin Peoples R China InferVision Beijing Peoples R China

ISBN: (纸本)9781728171685

As a serious infectious disease, tuberculosis (TB) is one of the major threats to human health worldwide, leading to millions of deaths every year. Although early diagnosis and treatment can greatly improve the chances of survival, it remains a major challenge, especially in developing countries. computer-aided tuberculosis diagnosis (CTD) is a promising choice for TB diagnosis due to the great successes of deep learning. However, when it comes to TB diagnosis, the lack of training data has hampered the progress of CTD. To solve this problem, we establish a large-scale TB dataset, namely Tuberculosis X-ray (TBX11K) dataset. This dataset contains 11200 X-ray images with corresponding bounding box annotations for TB areas, while the existing largest public TB dataset only has 662 X-ray images with corresponding image-level annotations. The proposed dataset enables the training of sophisticated detectors for high-quality CTD. We reform the existing object detectors to adapt them to simultaneous image classification and TB area detection. These reformed detectors are trained and evaluated on the proposed TBX11K dataset and served as the baselines for future research.

关键词： Deep learning

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Improving Adversarially Robust Few-shot Image Classification with Generalizable Representations

Improving Adversarially Robust Few-shot Image Classification...

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

作者： Dong, Junhao Wang, Yuan Lai, Jianhuang Xie, Xiaohua Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Guangdong Prov Key Lab Informat Secur Technol Guangzhou Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Few-Shot Image Classification (FSIC) aims to recognize novel image classes with limited data, which is significant in practice. In this paper, we consider the FSIC problem in the case of adversarial examples. This is an extremely challenging issue because current deep learning methods are still vulnerable when handling adversarial examples, even with massive labeled training samples. For this problem, existing works focus on training a network in the metalearning fashion that depends on numerous sampled fewshot tasks. In comparison, we propose a simple but effective baseline through directly learning generalizable representations without tedious task sampling, which is robust to unforeseen adversarial FSIC tasks. Specifically, we introduce an adversarial-aware mechanism to establish auxiliary supervision via feature-level differences between legitimate and adversarial examples. Furthermore, we design a novel adversarial-reweighted training manner to alleviate the imbalance among adversarial examples. The feature purifier is also employed as post-processing for adversarial features. Moreover, our method can obtain generalizable representations to remain superior transferability, even facing cross-domain adversarial examples. Extensive experiments show that our method can significantly outperform state-of-the-art adversarially robust FSIC methods on two standard benchmarks.

关键词： Training Deep learning computer vision Image recognition Benchmark testing pattern recognition Task analysis

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Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing 31

Weakly-Supervised Semantic Segmentation Network with Deep Se...

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31st ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Huang, Zilong Wang, Xinggang Wang, Jiasi Liu, Wenyu Wang, Jingdong Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan Hubei Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781538664209

This paper studies the problem of learning image semantic segmentation networks only using image-level labels as supervision, which is important since it can significantly reduce human annotation efforts. Recent state-of-the-art methods on this problem first infer the sparse and discriminative regions for each object class using a deep classification network, then train semantic a segmentation network using the discriminative regions as supervision. Inspired by the traditional image segmentation methods of seeded region growing, we propose to train a semantic segmentation network starting from the discriminative regions and progressively increase the pixel-level supervision using by seeded region growing. The seeded region growing module is integrated in a deep segmentation network and can benefit from deep features. Different from conventional deep networks which have fixed/static labels, the proposed weakly-supervised network generates new labels using the contextual information within an image. The proposed method significantly outperforms the weakly-supervised semantic segmentation methods using static labels, and obtains the state-of-the-art performance, which are 63.2% mIoU score on the PASCAL VOC 2012 test set and 26.0% mIoU score on the COCO dataset.

关键词： Image segmentation Semantics Training computer vision Visualization Task analysis Image color analysis

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ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Prior

ISNAS-DIP: Image-Specific Neural Architecture Search for Dee...

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

作者： Arican, Metin Ersin Kara, Ozgur Bredell, Gustav Konukoglu, Ender Bogazici Univ Dept Elect & Elect Engn Istanbul Turkey Swiss Fed Inst Technol Dept Informat Technol & Elect Engn Zurich Switzerland

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent works show that convolutional neural network (CNN) architectures have a spectral bias towards lower frequencies, which has been leveraged for various image restoration tasks in the Deep Image Prior (DIP) framework. The benefit of the inductive bias the network imposes in the DIP framework depends on the architecture. Therefore, researchers have studied how to automate the search to determine the best-performing model. However, common neural architecture search (NAS) techniques are resource and time-intensive. Moreover, best-performing models are determined for a whole dataset of images instead of for each image independently, which would be prohibitively expensive. In this work, we first show that optimal neural architectures in the DIP framework are image-dependent. Leveraging this insight, we then propose an image-specific NAS strategy for the DIP framework that requires substantially less training than typical NAS approaches, effectively enabling image-specific NAS. We justify the proposed strategy's effectiveness by (1) demonstrating its performance on a NAS Dataset for DIP that includes 522 models from a particular search space (2) conducting extensive experiments on image denoising, inpainting, and super-resolution tasks. Our experiments show that image-specific metrics can reduce the search space to a small cohort of models, of which the best model outperforms current NAS approaches for image restoration. Codes and datasets are available at https://***/ozgurkara99/ISNAS-DIP.

关键词： Training Computational modeling Superresolution computer architecture Image restoration pattern recognition Convolutional neural networks

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A Proposal-based Paradigm for Self-supervised Sound Source Localization in Videos

A Proposal-based Paradigm for Self-supervised Sound Source L...

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

作者： Xuan, Hanyu Wu, Zhiliang Yang, Jian Yan, Yan Alameda-Pineda, Xavier Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China IIT Dept Comp Sci Chicago IL 60616 USA Univ Grenoble Alpes LJK Grenoble INP INRIACNRS F-38000 Grenoble France

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Humans can easily recognize where and how the sound is produced via watching a scene and listening to corresponding audio cues. To achieve such cross-modal perception on machines, existing methods only use the maps generated by interpolation operations to localize the sound *** semantic object-level localization is more attractive for potential practical applications, we argue that these existing map-based approaches only provide a coarse-grained and indirect description of the sound source. In this paper,we advocate a novel proposal-based paradigm that can directly perform semantic object-level localization, without any manual annotations. We incorporate the global response map as an unsupervised spatial constraint to weight the proposals according to how well they cover the estimated global shape of the sound source. As a result, our proposal-based sound source localization can be cast into a simpler Multiple Instance Learning (MIL) problem by filtering those instances corresponding to large sound-unrelated regions. Our method achieves state-of-the-art (SOTA) performance when compared to several baselines on multiple datasets.

关键词： Location awareness Interpolation Annotations Shape Semantics Manuals pattern recognition

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Progressive Relation Learning for Group Activity recognition

Progressive Relation Learning for Group Activity Recognition

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

作者： Hu, Guyue Cui, Bo He, Yuan Yu, Shan Chinese Acad Sci CASIA Natl Lab Pattern Recognit Brainnetome Ctr Inst Automat Beijing Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781728171685

Group activities usually involve spatiotemporal dynamics among many interactive individuals, while only a few participants at several key frames essentially define the activity. Therefore, effectively modeling the group-relevant and suppressing the irrelevant actions (and interactions) are vital for group activity recognition. In this paper, we propose a novel method based on deep reinforcement learning to progressively refine the low-level features and high-level relations of group activities. Firstly, we construct a semantic relation graph (SRG) to explicitly model the relations among persons. Then, two agents adopting policy according to two Markov decision processes are applied to progressively refine the SRG. Specifically, one featured-istilling (FD) agent in the discrete action space refines the low-level spatiotemporal features by distilling the most informative frames. Another relation-gating (RG) agent in continuous action space adjusts the high-level semantic graph to pay more attention to group-relevant relations. The SRG, FD agent, and RG agent are optimized alternately to mutually boost the performance of each other. Extensive experiments on two widely used benchmarks demonstrate the effectiveness and superiority of the proposed approach.

关键词： Reinforcement learning

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Semantic Graph Convolutional Networks for 3D Human Pose Regression 32

Semantic Graph Convolutional Networks for 3D Human Pose Regr...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhao, Long Peng, Xi Tian, Yu Kapadia, Mubbasir Metaxas, Dimitris N. Rutgers State Univ New Brunswick NJ 22894 USA SUNY Binghamton Binghamton NY 13902 USA

ISBN: (纸本)9781728132938

In this paper, we study the problem of learning Graph Convolutional Networks (GCNs) for regression. Current architectures of GCNs are limited to the small receptive field of convolution filters and shared transformation matrix for each node. To address these limitations, we propose Semantic Graph Convolutional Networks (SemGCN), a novel neural network architecture that operates on regression tasks with graph-structured data. SemGCN learns to capture semantic information such as local and global node relationships, which is not explicitly represented in the graph. These semantic relationships can be learned through end-to-end training from the ground truth without additional supervision or hand-crafted rules. We further investigate applying SemGCN to 3D human pose regression. Our formulation is intuitive and sufficient since both 2D and 3D human poses can be represented as a structured graph encoding the relationships between joints in the skeleton of a human body. We carry out comprehensive studies to validate our method. The results prove that SemGCN outperforms state of the art while using 90% fewer parameters.

关键词： 3D from Single Image And Body Pose Face Gesture vision Applications and Systems

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