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Chen Qian
Researcher at SenseTime
Publications - 207
Citations - 10106
Chen Qian is an academic researcher from SenseTime. The author has contributed to research in topics: Computer science & Pose. The author has an hindex of 30, co-authored 125 publications receiving 5669 citations. Previous affiliations of Chen Qian include Shanghai Jiao Tong University & The Chinese University of Hong Kong.
Papers
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Journal ArticleDOI
Submission to Generic Event Boundary Detection Challenge@CVPR 2022: Local Context Modeling and Global Boundary Decoding Approach
TL;DR: A local context modeling sub-network is proposed to perceive diverse patterns of generic event boundaries, and it generates powerful video representations and reliable boundary confidence and global boundary decoding sub- network is exploited to decode event boundaries from a global view.
Proceedings Article
K-shot NAS: Learnable Weight-Sharing for NAS with K-shot Supernets
TL;DR: In this article, instead of counting on a single supernet, instead of taking their weights for each operation as a dictionary, the operation weight for each path is represented as a convex combination of items in a dictionary with a simplex code.
Journal ArticleDOI
Relational Self-Supervised Learning
TL;DR: This paper introduces a novel SSL paradigm, which is term as relational self-supervised learning (ReSSL) framework that learns representations by modeling the relationship between different instances by employing sharpened distribution of pairwise similarities among different instances as relation metric.
Journal ArticleDOI
Disarming visualization-based approaches in malware detection systems
TL;DR: In this article , a GAN-based architecture is proposed to generate variants of a malware in which the malware patterns found by visualization-based approaches are hidden, thus producing a new version of the malware that is not detected by both signature-based and visualizationbased techniques.
Proceedings Article
ReSSL: Relational Self-Supervised Learning with Weak Augmentation
TL;DR: Li et al. as mentioned in this paper proposed a relational self-supervised learning (ReSSL) framework, which employs sharpened distribution of pairwise similarities among different instances as \textit{relation} metric, which is thus utilized to match the feature embeddings of different augmentations.