313 citations
- Chinese University of Hong KongHK4 papers
- Sensetime (China)CN3 papers
- Tsinghua UniversityCN2 papers
- Beihang UniversityCN1 paper
- Beijing Academy of Artificial IntelligenceCN1 paper
- Beijing University of Posts and TelecommunicationsCN1 paper
- Chinese Academy of SciencesCN1 paper
- Nanyang Technological UniversitySG1 paper
- National Space Science CenterCN1 paper
- Peking UniversityCN1 paper
- Shanghai Artificial Intelligence LaboratoryCN1 paper
- Shanghai Jiao Tong UniversityCN1 paper
6 papers
Parameter-Inverted Image Pyramid Networks for Visual Perception and Multimodal Understanding
Zhaokai Wang, Xizhou Zhu, Xue Yang +8
Image pyramids are widely adopted in top-performing methods to obtain multi-scale features for precise visual perception and understanding. However, current image pyramids use the…
A Coarse-to-Fine Adaptive Network for Appearance-Based Gaze Estimation
Yihua Cheng, Shiyao Huang, Fei Wang +2
Human gaze is essential for various appealing applications. Aiming at more accurate gaze estimation, a series of recent works propose to utilize face and eye images simultaneously.…
Instance-level Human Parsing via Part Grouping Network
Ke Gong, Xiaodan Liang, Yicheng Li +3
Instance-level human parsing towards real-world human analysis scenarios is still under-explored due to the absence of sufficient data resources and technical difficulty in parsing…
Zoom-Net: Mining Deep Feature Interactions for Visual Relationship Recognition
Guojun Yin, Lu Sheng, Bin Liu +4
Recognizing visual relationships <subject-predicate-object> among any pair of localized objects is pivotal for image understanding. Previous studies have shown remarkable progress…
Exploring Disentangled Feature Representation Beyond Face Identification
Yu Liu, Fangyin Wei, Jing Shao +3
This paper proposes learning disentangled but complementary face features with minimal supervision by face identification. Specifically, we construct an identity Distilling and Dis…
Residual Attention Network for Image Classification
Fei Wang, Mengqing Jiang, Chen Qian +5
In this work, we propose "Residual Attention Network", a convolutional neural network using attention mechanism which can incorporate with state-of-art feed forward network archite…