961 citations
- Nankai UniversityCN66 papers
- Chinese Academy of SciencesCN51 papers
- National University of SingaporeSG41 papers
- Tsinghua UniversityCN35 papers
- Peng Huanwu Center for Fundamental TheoryCN26 papers
- Peking UniversityCN23 papers
- Nanyang Technological UniversitySG22 papers
- Centre National de la Recherche ScientifiqueFR20 papers
- University of Science and Technology of ChinaCN19 papers
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15 papers · 2 filters
Exploiting Operation Importance for Differentiable Neural Architecture Search
Xukai Xie, Yuan Zhou, Sun-Yuan Kung
Recently, differentiable neural architecture search methods significantly reduce the search cost by constructing a super network and relax the architecture representation by assign…
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
Zhaohui Zheng, Ping Wang, Wei Liu +3
Bounding box regression is the crucial step in object detection. In existing methods, while -norm loss is widely adopted for bounding box regression, it is not tailored to…
Semi-Heterogeneous Three-Way Joint Embedding Network for Sketch-Based Image Retrieval
Jianjun Lei, Yuxin Song, Bo Peng +3
Sketch-based image retrieval (SBIR) is a challenging task due to the large cross-domain gap between sketches and natural images. How to align abstract sketches and natural images i…
Comb Convolution for Efficient Convolutional Architecture
Dandan Li, Yuan Zhou, Shuwei Huo +1
Convolutional neural networks (CNNs) are inherently suffering from massively redundant computation (FLOPs) due to the dense connection pattern between feature maps and convolution…
Facial Expression Restoration Based on Improved Graph Convolutional Networks
Zhilei Liu, Le Li, Yunpeng Wu +1
Facial expression analysis in the wild is challenging when the facial image is with low resolution or partial occlusion. Considering the correlations among different facial local r…
Relation Modeling with Graph Convolutional Networks for Facial Action Unit Detection
Zhilei Liu, Jiahui Dong, Cuicui Zhang +2
Most existing AU detection works considering AU relationships are relying on probabilistic graphical models with manually extracted features. This paper proposes an end-to-end deep…