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检索条件"机构=Shenzhen Key lab of Computer Vision Pattern Recognition"
178 条 记 录,以下是51-60 订阅
排序:
Digging into uncertainty in self-supervised multi-view stereo
arXiv
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arXiv 2021年
作者: Xu, Hongbin Zhou, Zhipeng Wang, Yali Kang, Wenxiong Sun, Baigui Li, Hao Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences South China University of Technology Shanghai AI Laboratory Alibaba Group Pazhou Laboratory
Self-supervised Multi-view stereo (MVS) with a pretext task of image reconstruction has achieved significant progress recently. However, previous methods are built upon intuitions, lacking comprehensive explanations a... 详细信息
来源: 评论
Efficient Image Super-Resolution Using Pixel Attention  16th
Efficient Image Super-Resolution Using Pixel Attention
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Workshops held at the 16th European Conference on computer vision, ECCV 2020
作者: Zhao, Hengyuan Kong, Xiangtao He, Jingwen Qiao, Yu Dong, Chao ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China University of Chinese Academy of Sciences Beijing China
This work aims at designing a lightweight convolutional neural network for image super resolution (SR). With simplicity bare in mind, we construct a pretty concise and effective network with a newly proposed pixel att... 详细信息
来源: 评论
Conditional Sequential Modulation for Efficient Global Image Retouching  16th
Conditional Sequential Modulation for Efficient Global Image...
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16th European Conference on computer vision, ECCV 2020
作者: He, Jingwen Liu, Yihao Qiao, Yu Dong, Chao ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT - SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China University of Chinese Academy of Sciences Beijing China
Photo retouching aims at enhancing the aesthetic visual quality of images that suffer from photographic defects such as over/under exposure, poor contrast, inharmonious saturation. Practically, photo retouching can be... 详细信息
来源: 评论
Learning to Predict Context-Adaptive Convolution for Semantic Segmentation  16th
Learning to Predict Context-Adaptive Convolution for Semanti...
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16th European Conference on computer vision, ECCV 2020
作者: Liu, Jianbo He, Junjun Qiao, Yu Ren, Jimmy S. Li, Hongsheng CUHK-SenseTime Joint Laboratory The Chinese University of Hong Kong Hong Kong Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China SenseTime Research Hong Kong
Long-range contextual information is essential for achieving high-performance semantic segmentation. Previous feature re-weighting methods demonstrate that using global context for re-weighting feature channels can ef... 详细信息
来源: 评论
PC-HMR: Pose calibration for 3d human mesh recovery from 2D images/videos
arXiv
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arXiv 2021年
作者: Luan, Tianyu Wang, Yali Zhang, Junhao Wang, Zhe Zhou, Zhipeng Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China University of California Irvine United States
The end-to-end Human Mesh Recovery (HMR) approach (Kanazawa et al. 2018) has been successfully used for 3D body reconstruction. However, most HMR-based frameworks reconstruct human body by directly learning mesh param... 详细信息
来源: 评论
EfficientFCN: Holistically-Guided Decoding for Semantic Segmentation  1
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16th European Conference on computer vision, ECCV 2020
作者: Liu, Jianbo He, Junjun Zhang, Jiawei Ren, Jimmy S. Li, Hongsheng CUHK-SenseTime Joint Laboratory The Chinese University of Hong Kong Shatin Hong Kong Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China SenseTime Research Beijing China
Both performance and efficiency are important to semantic segmentation. State-of-the-art semantic segmentation algorithms are mostly based on dilated Fully Convolutional Networks (dilatedFCN), which adopt dilated conv... 详细信息
来源: 评论
Neighbourhood-guided feature reconstruction for occluded person re-identification
arXiv
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arXiv 2021年
作者: Yu, Shijie Chen, Dapeng Zhao, Rui Chen, Haobin Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences China SenseTime Group Limited Shanghai AI Lab Shanghai China
Person images captured by surveillance cameras are often occluded by various obstacles, which lead to defective feature representation and harm person re-identification (Re-ID) performance. To tackle this challenge, w... 详细信息
来源: 评论
Tensor Low-Rank Reconstruction for Semantic Segmentation  1
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16th European Conference on computer vision, ECCV 2020
作者: Chen, Wanli Zhu, Xinge Sun, Ruoqi He, Junjun Li, Ruiyu Shen, Xiaoyong Yu, Bei The Chinese University of Hong Kong New Territories Hong Kong Shanghai Jiao Tong University Shanghai China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China SmartMore Shenzhen China
Context information plays an indispensable role in the success of semantic segmentation. Recently, non-local self-attention based methods are proved to be effective for context information collection. Since the desire... 详细信息
来源: 评论
Learning Discriminative Representation For Facial Expression recognition From Uncertainties
Learning Discriminative Representation For Facial Expression...
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IEEE International Conference on Image Processing
作者: Xingyu Fan Zhongying Deng Kai Wang Xiaojiang Peng Yu Qiao Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China
Recent progresses on Facial Expression recognition (FER) heavily rely on deep learning models trained with large scale datasets. However, large-scale facial expression datasets always suffer from annotation uncertaint... 详细信息
来源: 评论
UniFormer: Unifying Convolution and Self-attention for Visual recognition
arXiv
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arXiv 2022年
作者: Li, Kunchang Wang, Yali Zhang, Junhao Gao, Peng Song, Guanglu Liu, Yu Li, Hongsheng Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China National University of Singapore Singapore Shanghai Artificial Intelligence Laboratory China SenseTime Research China The Chinese University of Hong Kong Hong Kong
It is a challenging task to learn discriminative representation from images and videos, due to large local redundancy and complex global dependency in these visual data. Convolution neural networks (CNNs) and vision t... 详细信息
来源: 评论