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Gangshan Wu
Researcher at Nanjing University
Publications - Â 178
Citations - Â 3183
Gangshan Wu is an academic researcher from Nanjing University. The author has contributed to research in topics: Computer science & Minimum bounding box. The author has an hindex of 20, co-authored 148 publications receiving 1550 citations.
Papers
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Proceedings ArticleDOI
Depth saliency based on anisotropic center-surround difference
TL;DR: A novel saliency method that works on depth images based on anisotropic center-surround difference is proposed, which measures the saliency of a point by how much it outstands from surroundings, which takes the global depth structure into consideration.
Proceedings ArticleDOI
TDN: Temporal Difference Networks for Efficient Action Recognition
TL;DR: Temporal Difference Network (TDN) as discussed by the authors is proposed to capture multi-scale temporal information for efficient action recognition by leveraging a temporal difference operator, and systematically assess its effect on short-term and long-term motion modeling.
Proceedings ArticleDOI
Residual Feature Aggregation Network for Image Super-Resolution
TL;DR: This work proposes a novel residual feature aggregation (RFA) framework for more efficient feature extraction and proposes an enhanced spatial attention (ESA) block to make the residual features to be more focused on critical spatial contents.
Proceedings ArticleDOI
Learning Actor Relation Graphs for Group Activity Recognition
TL;DR: Zhang et al. as discussed by the authors proposed a flexible and efficient actor relation graph (ARG) to simultaneously capture the appearance and position relation between actors, which can be used for recognizing group activity in multi-person scenes.
Posted Content
Residual Feature Distillation Network for Lightweight Image Super-Resolution
Jie Liu,Jie Tang,Gangshan Wu +2 more
TL;DR: The feature distillation connection (FDC) is proposed that is functionally equivalent to the channel splitting operation while being more lightweight and flexible and can rethink the information multi-distillation network (IMDN) and propose a lightweight and accurate SISR model called residual feature distilling network (RFDN).