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Shuo Yang
Researcher at Amazon.com
Publications - 57
Citations - 8687
Shuo Yang is an academic researcher from Amazon.com. The author has contributed to research in topics: Computer science & Object detection. The author has an hindex of 19, co-authored 37 publications receiving 6096 citations. Previous affiliations of Shuo Yang include Tsinghua University & The Chinese University of Hong Kong.
Papers
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Proceedings ArticleDOI
Residual Attention Network for Image Classification
Fei Wang,Mengqing Jiang,Chen Qian,Shuo Yang,Cheng Li,Honggang Zhang,Xiaogang Wang,Xiaoou Tang +7 more
TL;DR: Residual Attention Network as mentioned in this paper is a convolutional neural network using attention mechanism which can incorporate with state-of-the-art feed forward network architecture in an end-to-end training fashion.
Proceedings ArticleDOI
WIDER FACE: A Face Detection Benchmark
TL;DR: There is a gap between current face detection performance and the real world requirements, and the WIDER FACE dataset, which is 10 times larger than existing datasets is introduced, which contains rich annotations, including occlusions, poses, event categories, and face bounding boxes.
Posted Content
Residual Attention Network for Image Classification
Fei Wang,Mengqing Jiang,Chen Qian,Shuo Yang,Cheng Li,Honggang Zhang,Xiaogang Wang,Xiaoou Tang +7 more
TL;DR: Residual Attention Network as discussed by the authors is a convolutional neural network using attention mechanism which can incorporate with state-of-the-art feed forward network architecture in an end-to-end training fashion.
Proceedings ArticleDOI
From Facial Parts Responses to Face Detection: A Deep Learning Approach
TL;DR: Zhang et al. as mentioned in this paper proposed a novel deep convolutional network (DCN) that achieves outstanding performance on FDDB, PASCAL Face, and AFW, achieving a high recall rate of 90.99% on the challenging FDDB benchmark, outperforming the state-of-the-art method.
Proceedings ArticleDOI
Region Proposal by Guided Anchoring
TL;DR: This paper presents an alternative scheme, named Guided Anchoring, which leverages semantic features to guide the anchoring, and jointly predicts the locations where the center of objects of interest are likely to exist as well as the scales and aspect ratios at different locations.