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检索条件"机构=Shenzhen Key Lab for Computer Vision and Pattern Recognition"
178 条 记 录,以下是111-120 订阅
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Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers
Modulating Image Restoration with Continual Levels via Adapt...
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IEEE/CVF Conference on computer vision and pattern recognition
作者: Jingwen He Chao Dong Yu Qiao ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences
In image restoration tasks, like denoising and super-resolution, continual modulation of restoration levels is of great importance for real-world applications, but has failed most of existing deep learning based image... 详细信息
来源: 评论
MetaCleaner: Learning to Hallucinate Clean Representations for Noisy-labeled Visual recognition
MetaCleaner: Learning to Hallucinate Clean Representations f...
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IEEE/CVF Conference on computer vision and pattern recognition
作者: Weihe Zhang Yali Wang Yu Qiao Shenzhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences
Deep Neural Networks (DNNs) have achieved remarkable successes in large-scale visual recognition. However, they often suffer from overfitting under noisy labels. To alleviate this problem, we propose a conceptually si... 详细信息
来源: 评论
Multi-dimension modulation for image restoration with dynamic controllable residual Learning
arXiv
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arXiv 2019年
作者: He, Jingwen Dong, Chao Qiaoy, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China
Based on the great success of deterministic learning, to interactively control the output effects has attracted increasingly attention in the image restoration field. The goal is to generate continuous restored images... 详细信息
来源: 评论
BasicVSR: The search for essential components in video super-resolution and beyond
arXiv
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arXiv 2020年
作者: Chan, Kelvin C.K. Wang, Xintao Yu, Ke Dong, Chao Loy, Chen Change S-Lab Nanyang Technological University Singapore Applied Research Center Tencent PCG CUHK – SenseTime Joint Lab Chinese University of Hong Kong Shenzhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China
Video super-resolution (VSR) approaches tend to have more components than the image counterparts as they need to exploit the additional temporal dimension. Complex designs are not uncommon. In this study, we wish to u... 详细信息
来源: 评论
A comprehensive study on temporal modeling for online action detection
arXiv
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arXiv 2020年
作者: Wang, Wen Peng, Xiaojiang Qiao, Yu Cheng, Jian School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu Sichuan611731 China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences
—Online action detection (OAD) is a practical yet challenging task, which has attracted increasing attention in recent years. A typical OAD system mainly consists of three modules: a frame-level feature extractor whi... 详细信息
来源: 评论
DF2Net: A Dense-Fine-Finer Network for Detailed 3D Face Reconstruction
DF2Net: A Dense-Fine-Finer Network for Detailed 3D Face Reco...
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International Conference on computer vision (ICCV)
作者: Xiaoxing Zeng Xiaojiang Peng Yu Qiao ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology University of Chinese Academy of Sciences China
Reconstructing the detailed geometric structure from a single face image is a challenging problem due to its ill-posed nature and the fine 3D structures to be recovered. This paper proposes a deep Dense-Fine-Finer Net... 详细信息
来源: 评论
PA3D: Pose-Action 3D Machine for Video recognition
PA3D: Pose-Action 3D Machine for Video Recognition
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IEEE/CVF Conference on computer vision and pattern recognition
作者: An Yan Yali Wang Zhifeng Li Yu Qiao Shenzhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Tencent AI Lab
Recent studies have witnessed the successes of using 3D CNNs for video action recognition. However, most 3D models are built upon RGB and optical flow streams, which may not fully exploit pose dynamics, i.e., an impor... 详细信息
来源: 评论
Image quality assessment for perceptual image restoration: A new dataset, benchmark and metric
arXiv
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arXiv 2020年
作者: Gu, Jinjin Cai, Haoming Chen, Haoyu Ye, Xiaoxing Ren, Jimmy S. Dong, Chao School of Electrical and Information Engineering University of Sydney Australia Chinese University of Hong Kong Shenzhen Hong Kong ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences SenseTime Research Qing Yuan Research Institute Shanghai Jiao Tong University Shanghai China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society
Image quality assessment (IQA) is the key factor for the fast development of image restoration (IR) algorithms. The most recent perceptual IR algorithms based on generative adversarial networks (GANs) have brought in ... 详细信息
来源: 评论
Frame attention networks for facial expression recognition in videos
arXiv
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arXiv 2019年
作者: Meng, Debin Peng, Xiaojiang Wang, Kai Qiao, Yu Shenzhen Institutes of Advanced Technology Chinese Academy of Science Shenzhen China Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen China University of Chinese Academy of Sciences Beijing China
The video-based facial expression recognition aims to classify a given video into several basic emotions. How to integrate facial features of individual frames is crucial for this task. In this paper, we propose the F... 详细信息
来源: 评论
RankSRGAN: Generative Adversarial Networks With Ranker for Image Super-Resolution
RankSRGAN: Generative Adversarial Networks With Ranker for I...
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International Conference on computer vision (ICCV)
作者: Wenlong Zhang Yihao Liu Chao Dong Yu Qiao ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences
Generative Adversarial Networks (GAN) have demonstrated the potential to recover realistic details for single image super-resolution (SISR). To further improve the visual quality of super-resolved results, PIRM2018-SR... 详细信息
来源: 评论