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Bing Shuai
Researcher at Amazon.com
Publications - 55
Citations - 7559
Bing Shuai is an academic researcher from Amazon.com. The author has contributed to research in topics: Convolutional neural network & Computer science. The author has an hindex of 22, co-authored 49 publications receiving 5155 citations. Previous affiliations of Bing Shuai include Nanyang Technological University.
Papers
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Journal ArticleDOI
Recent advances in convolutional neural networks
Jiuxiang Gu,Zhenhua Wang,Jason Kuen,Lianyang Ma,Amir Shahroudy,Bing Shuai,Ting Liu,Xingxing Wang,Gang Wang,Jianfei Cai,Tsuhan Chen +10 more
TL;DR: A broad survey of the recent advances in convolutional neural networks can be found in this article, where the authors discuss the improvements of CNN on different aspects, namely, layer design, activation function, loss function, regularization, optimization and fast computation.
Posted Content
Recent Advances in Convolutional Neural Networks
Jiuxiang Gu,Zhenhua Wang,Jason Kuen,Lianyang Ma,Amir Shahroudy,Bing Shuai,Ting Liu,Xingxing Wang,Li Wang,Gang Wang,Jianfei Cai,Tsuhan Chen +11 more
TL;DR: This paper details the improvements of CNN on different aspects, including layer design, activation function, loss function, regularization, optimization and fast computation, and introduces various applications of convolutional neural networks in computer vision, speech and natural language processing.
Posted Content
A Siamese Long Short-Term Memory Architecture for Human Re-Identification
TL;DR: A novel siamese Long Short-Term Memory (LSTM) architecture that can process image regions sequentially and enhance the discriminative capability of local feature representation by leveraging contextual information.
Proceedings ArticleDOI
Context Contrasted Feature and Gated Multi-scale Aggregation for Scene Segmentation
TL;DR: A novel context contrasted local feature that not only leverages the informative context but also spotlights the local information in contrast to the context is proposed that greatly improves the parsing performance.
Book ChapterDOI
A Siamese Long Short-Term Memory Architecture for Human Re-identification
TL;DR: Wang et al. as mentioned in this paper proposed a siamese Long Short-Term Memory (LSTM) architecture that can process image regions sequentially and enhance the discriminative capability of local feature representation by leveraging contextual information.