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检索条件"任意字段=4th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2021"

共 181 条记录，以下是31-40 订阅

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TransDDPM: Transformer-Based Denoising Diffusion Probabilistic Model for Image Restoration 6th

TransDDPM: Transformer-Based Denoising Diffusion Probabilist...

引用

6th chinese conference on pattern recognition and computer vision (prcv)

作者： Wei, Pan Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9789819985517;9789819985524

Although diffusion models have achieved impressive success for image generation, its application for image restoration is still underexplored. Following tremendous success in natural language processing, transformers have also shown great success for computer vision. Although several researches indicate that increasing transformer depth/width improves the applicability of diffusion models, application of Transformers in diffusion models is still underexplored due to quadratic complexity with the spatial resolution. In this work, we proposed a Transformer-based Denoising Diffusion Probabilistic Model (TransDDPM) for image restoration. With multi-head cross-covariance attention (MXCA), TransDDPM can operates global self-attention with cross-covariance matrix in channel dimension rather than spatial dimension. Another gated feed-forward network (GFFN) is included to enhance the ability to exploit spatial local context. Powered by these designs, TransDDPM is capable for both long-range dependencies and short-range dependencies and flexible for images of various resolutions. Comprehensive experiments demonstrate our TransDDPM achieves state-of-the art performance on several restoration tasks, e.g., image deraining, image dehazing and motion deblurring.

关键词： DDPM Cross-Covariance Attention Image Restoration

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SimplePose V2: Greedy Offset-Guided Keypoint Grouping for Human Pose Estimation 4th

SimplePose V2: Greedy Offset-Guided Keypoint Grouping for Hu...

引用

4th chinese conference on pattern recognition and computer vision (prcv)

作者： Li, Jia Xiang, Linhua Chen, Jiwei Wang, Zengfu Univ Sci & Technol China Dept Automat Hefei Peoples R China Chinese Acad Sci Inst Intelligent Machines Hefei Peoples R China

ISBN: (纸本)9783030880040;9783030880033

We propose a simple yet reliable bottom-up approach with a good trade-off between accuracy and efficiency for the problem of multi person pose estimation. To encode the multi-person pose information in the image, we employ Gaussian response heatmaps to encode the key point position information of all persons. And we propose a set of guiding offsets to encode the pairing information between keypoints belonging to the same individuals. We use an Hourglass Network to infer the said heatmaps and guiding offsets simultaneously. During testing, we greedily assign the detected keypoints to different individuals according to the guiding offsets. Besides, we introduce a peak regularization into the pixel-wise L-2 loss for keypoint heatmap regression, improving the precision of keypoint localization. Experiments validate the effectiveness of the introduced components. Our approach is comparable to the state of the art on the challenging COCO dataset under fair conditions.

关键词： Bottom-up Human pose Guiding offset Heatmap

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High-Performance Discriminative Tracking with Target-Aware Feature Embeddings 4th

High-Performance Discriminative Tracking with Target-Aware F...

引用

4th chinese conference on pattern recognition and computer vision (prcv)

作者： Yu, Bin Tang, Ming Zheng, Linyu Zhu, Guibo Wang, Jinqiao Lu, Hanqing Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit 95 Zhongguancun East Rd Beijing 100190 Peoples R China ObjectEye Inc Beijing Peoples R China

ISBN: (纸本)9783030880040;9783030880033

Discriminative model-based trackers have made remarkable progress recently. However, due to the extreme imbalance of foreground and background samples, the learned model is hard to fit the training samples well in the online tracking. In this paper, to alleviate the negative influence caused by the imbalance issue, we propose a novel construction scheme of target-aware features for online discriminative tracking. Specifically, we design a sub-network to generate target-aware feature embeddings of foregrounds and backgrounds by projecting the learned feature embeddings into the target-aware feature space. then, a model solver, which is integrated into our networks, is applied to learn the discriminative model. Based on such feature construction, the learned model is able to fit training samples well in the online tracking. Experimental results on four benchmarks, OTB-2015, VOT-2018, NfS, and GOT-10k, show that the proposed target-aware feature construction is effective for visual tracking, leading to the high-performance of our tracker.

关键词： Visual tracking Data imbalance Discriminative model

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An Improved Tensor Network for Image Classification in Histopathology 5th

An Improved Tensor Network for Image Classification in Histo...

引用

5th chinese conference on pattern recognition and computer vision (prcv)

作者： Fan, Yongxian Gong, Hao Guilin Univ Elect Technol Guilin 541004 Peoples R China

ISBN: (纸本)9783031189098;9783031189104

As some machine learning tools such as neural networks are adopted in several fields, some interesting connections and comparisons of concepts between these fields are beginning to emerge. Tensor networks have been an enabling tool for the analysis of quantum many-body systems in physics in the past and now are applied to medical image analysis tasks. through inter-domain concept migration, we propose an Unordered Stacked Tensor Network model (UnSTNet). We extend the matrix product state to adapt to the medical image analysis task, and embed the classical image domain concept, local disorder of images, to preserve the global structure of images. In addition, we stack unordered blocks within tensor blocks to integrate global information, and stack the outputs of multiple tensor blocks to fuse image features in different states for global evaluation. We evaluate on three publicly available histopathology image datasets and demonstrate that the proposed method obtains improved performance compared to related tensor learning methods. the results also show that, compared with the advanced deep learning method, our model can perform well with fewer computing resources and hyperparameters.

关键词： Histopathology image pattern recognition and classification Tensor network

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Rider Re-identification Based on Pyramid Attention 5th

Rider Re-identification Based on Pyramid Attention

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5th chinese conference on pattern recognition and computer vision (prcv)

作者： Li, Jiaze Liu, Bin Univ Sci & Technol China Hefei Peoples R China Chinese Acad Sci Key Lab Electromagnet Space Informat Hefei Peoples R China

ISBN: (纸本)9783031189067;9783031189074

In recent years, object re-identification (ReID) based on deep learning has made great progress, and research in this field mainly focuses on person and vehicle. However, the researchers ignore an important target: riders. Electric bikes are an essential part of modern transportation scenarios, so identifying riders and monitoring their behavior on a large scale is critical to public safety management. To bridge the research gap of rider ReID, this paper proposes a pyramid attention network (PANet), which utilizes the pyramid structure to capture multi-scale clues and discovers key regional features from fine to coarse. PANet first learns fine-grained attention in local small regions, then gradually aggregates local regions to expand the scope of attention exploration, and finally conducts global coarse-grained attention learning. We implement two different dimensional attention computations in the pyramid attention network: spatial attention and channel attention. Experiments on BPReID and MoRe datasets demonstrate the effectiveness of this network design, which can achieve better performance with limited computational overhead.

关键词： Rider re-identification Pyramid attention Deep learning

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Couple Double-Stage FPNs with Single Pipe-Line for Solar Speckle Images Deblurring 4th

Couple Double-Stage FPNs with Single Pipe-Line for Solar Spe...

引用

4th chinese conference on pattern recognition and computer vision (prcv)

作者： Li, Fuhai Jiang, Murong Yang, Lei Yunnan Univ Sch Informat Sci & Engn Kunming Yunnan Peoples R China Chinese Acad Sci Yunnan Observ Kunming Yunnan Peoples R China

ISBN: (纸本)9783030880132;9783030880125

Solar speckle images acquired by ground-based optical telescope usually are blurred or degraded seriously with more noise and fuzzy local details. Most of the deep learning deblurring methods are suitable for the natural images which have sufficient contextualized and gradient information, but for solar speckle images may cause some problems such as over-smoothing, high-frequency loss, and artifacts generated. In this paper, we propose a deblurring method based on coupling double-stage feature pyramid networks (FPN) with a single pipe-line (DSFSP) to reconstruct high-resolution solar speckle images. In stage1, one FPN is used to recover structure features;In stage2, another FPN is used to enhance the structural contextualized, and the single pipe-line coupled with this FPN is used to extract gradient information. After fusing these to generate a reconstructed image, discriminators are used to make it closer to the reference. Experiments show that DSFSP has a strong ability to strengthen the gradient spatial and contextualized information, improve image clarity, restore high-frequency details and drop artifacts.

关键词： Double-stage FPN Single pipe-line Gradient spatial Contextualized information Solar speckle image

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GridIIS: Grid Based Interactive Image Segmentation 6th

GridIIS: Grid Based Interactive Image Segmentation

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6th chinese conference on pattern recognition and computer vision (prcv)

作者： Zhu, Pengqi Wang, Da-Han Zhu, Shunzhi Xiaman Univ Technol Sch Comp & Informat Engn Xiamen Peoples R China Fujian Key Lab Pattern Recognit & Image Understan Xiamen Peoples R China

ISBN: (纸本)9789819985517;9789819985524

Interactive segmentation enables users to specify the object of interest (OOI) via various interaction strategies to obtain accurate segmentation results. An ideal interactive method should efficiently and accurately express users' segmentation intentions. However, the existing methods can only use a single interactive mode, ignoring the differences in scale and shape between OOIs, resulting in an inflexibility labeling process. In this paper, we propose a grid-based interactive image segmentation method (GridIIS). Specifically, GridIIS overlays grids on the image, and users can specify the location and shape of the OOI by selecting the grid areas as the interactive guidance. Users can choose the appropriate grid selection method and size considering the OOI's scale, shape, and boundary clarity to obtain guidance. We accordingly propose a novel grid sampling strategy, that considers the OOI's scale and shapes to adaptively estimate the grid size and area. Experiments on several datasets from different domains (street views, medical images, scene texts, etc.) show that our method achieves superior performance with fewer interaction rounds and exhibits strong generalization ability in cross-domain datasets.

关键词： Interactive segmentation Grid-based interactive

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CHENet: Image to Image chinese Handwriting Eraser 5th

CHENet: Image to Image Chinese Handwriting Eraser

引用

5th chinese conference on pattern recognition and computer vision (prcv)

作者： Wang, Biao Li, Jiayi Jin, Xin Yuan, Qiong Beijing Elect Sci & Technol Inst Beijing Peoples R China

ISBN: (纸本)9783031189159;9783031189166

Erasing chinese handwritten words in images has many applications in our daily life. It can hide the private information in the archive images (home address, identity information), delete the signature information in the contract images, and clear the handwriting in the test paper to reuse it. However, the existing research mainly focuses on scene text removal. To fill this gap and facilitate this research direction, we propose a novel model called CHENet (chinese Handwriting Eraser Network) that can automatically remove chinese handwriting located on the images. In particular, we design two modules: CAB (Convolution Attention Block) and CAP (Chain Attention Pooling) that can improve the ability of the CHENet. Also, for information protection, we collect images with chinese handwritten words from the archive, contract, and exam papers, and build the CH-dataset that consists of 1623 scrupulously annotated images. Qualitative and quantitative evaluations show that our CHENet can solve this task well.

关键词： Text removal chinese handwriting removal End-to-end network Attention network

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Exploiting Robust Memory Features for Unsupervised Reidentification 5th

Exploiting Robust Memory Features for Unsupervised Reidentif...

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5th chinese conference on pattern recognition and computer vision (prcv)

作者： Lian, Jiawei Wang, Da-Han Du, Xia Wu, Yun Zhu, Shunzhi Xiamen Univ Technol Sch Comp & Informat Engn Xiamen 361024 Peoples R China Fujian Key Lab Pattern Recognit & Image Understan 600 Ligong Rd Xiamen 361024 Peoples R China

ISBN: (纸本)9783031189098;9783031189104

Unsupervised re-identification (ReID) is a task that does not use labels for classification and recognition. It is fundamentally challenging due to the need to retrieve target objects across different perspectives, and coupled with the absence of ID labels for supervision. However, the most severe aspect is that the appearance features of vehicles can be shifted under different viewpoints, which may cause an imbalance in inter-class and intra-class variation. In this work, we propose a fully unsupervised re-identification method applied to the person and vehicle domain. In particular, different from previous methods of cluster pseudolabelling, we compress the feature dimensions used to generate the labels and improve the quality of the pseudo-label. then, in the update memory feature module, we introduce the idea of partitioning to construct algorithms for dynamically finding inter-class and intra-class boundaries to improve the robustness of the model. To demonstrate the effectiveness of the proposed method, we conduct experiments on one vehicle dataset (VeRi-776) and one person datasets (MSMT17). Experimental results demonstrate that our method is effective in enhancing the performance of the ReID task, and the proposed method achieves the state-of-the-art performance. the code has been made available at https://***/ljwwwiop/unsupervised reid.

关键词： Re-identification Intelligent transport systems Unsupervised learning computer vision Deep learning

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Temporal Correlation-Diversity Representations for Video-Based Person Re-Identification 5th

Temporal Correlation-Diversity Representations for Video-Bas...

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5th chinese conference on pattern recognition and computer vision (prcv)

作者： Gong, Litong Zhang, Ruize Tang, Sheng Cao, Juan Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Res Inst Intelligent Comp Zhejiang Lab Hangzhou Zhejiang Peoples R China Hangzhou Zhongke Ruijian Technol Co Ltd Hangzhou Peoples R China

ISBN: (纸本)9783031189067;9783031189074

Video-based person re-identification is a challenging task due to illuminations, occlusions, viewpoint changes, and pedestrian misalignment. Most previous works focus more on temporal correlation features, which leads to a lack of detailed information. In this paper, we emphasize the importance of keeping both correlation and diversity of multiframe features simultaneously. thus, we propose a Temporal Correlation-Diversity Representation (TCDR) network to enhance the representation of frame-level pedestrian features and the temporal feature aggregation abilities. Specifically, in order to capture correlated but diverse temporal features, we propose a Temporal-Guided Frame Feature Enhancement (TGFE) module, which explores the temporal correlation with a global perspective and enhances frame-level features to achieve the temporal diversity. Furthermore, we propose a Temporal Feature Integration (TFI) module to aggregate multi-frame features. Finally, we propose a novel progressive smooth loss to alleviate the influence of noisy frames. Extensive experiments show that our method achieves the state-of-the-art performance on MARS, DukeMTMC-VideoReID and LS-VID datasets.

关键词： Person re-identification Feature enhancement Deep learning

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