G
Gang Sun
Researcher at Chinese Academy of Sciences
Publications - 14
Citations - 21176
Gang Sun is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Convolutional neural network & Feature (computer vision). The author has an hindex of 7, co-authored 12 publications receiving 8592 citations. Previous affiliations of Gang Sun include SenseTime.
Papers
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Journal ArticleDOI
Squeeze-and-Excitation Networks
TL;DR: This work proposes a novel architectural unit, which is term the "Squeeze-and-Excitation" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels and finds that SE blocks produce significant performance improvements for existing state-of-the-art deep architectures at minimal additional computational cost.
Posted Content
Squeeze-and-Excitation Networks
TL;DR: Squeeze-and-excitation (SE) as mentioned in this paper adaptively recalibrates channel-wise feature responses by explicitly modeling interdependencies between channels, which can be stacked together to form SENet architectures.
Posted Content
Deep Image: Scaling up Image Recognition
TL;DR: A state-of-the-art image recognition system, Deep Image, developed using end-to-end deep learning, which achieves excellent results on multiple challenging computer vision benchmarks.
Proceedings ArticleDOI
A Key Volume Mining Deep Framework for Action Recognition
TL;DR: A key volume mining deep framework to identify key volumes and conduct classification simultaneously and an effective yet simple "unsupervised key volume proposal" method for high quality volume sampling are proposed.
Proceedings Article
Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks
TL;DR: Gathering and Excite as mentioned in this paper proposes a pair of operators: gather and excite, which redistributes the pooled information to local features, which can be integrated directly in existing architectures to improve their performance.