J
Jianchao Yang
Researcher at Adobe Systems
Publications - 184
Citations - 28091
Jianchao Yang is an academic researcher from Adobe Systems. The author has contributed to research in topics: Convolutional neural network & Sparse approximation. The author has an hindex of 60, co-authored 183 publications receiving 24321 citations. Previous affiliations of Jianchao Yang include University of Illinois at Urbana–Champaign.
Papers
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Surveillance Event Detection.
Mert Dikmen,Huazhong Ning,Dennis Lin,Liangliang Cao,Vuong Le,Shen-Fu Tsai,Kai-Hsiang Lin,Zhen Li,Jianchao Yang,Thomas S. Huang,Fengjun Lv,Wei Xu,Ming Yang,Kai Yu,Zhao Zhao,Guangyu Zhu,Yihong Gong +16 more
TL;DR: Three generalized systems for event detection are developed and evaluated, and two ad-hoc methods that were designed to specifically detect OpposingFlow and TakePicture events are introduced.
Book ChapterDOI
\ell ^{0}-Sparse Subspace Clustering
TL;DR: It is proved that subspace-sparse representation, a key element in subspace clustering, can be obtained by \(\ell ^{0}\)-SSC for arbitrary distinct underlying subspaces almost surely under the mild i.i.d. assumption on the data generation.
Proceedings ArticleDOI
Intra-frame deblurring by leveraging inter-frame camera motion
Haichao Zhang,Jianchao Yang +1 more
TL;DR: The proposed video deblurring method effectively leverages the information distributed across multiple video frames due to camera motion, jointly estimating the motion between consecutive frames and blur within each frame.
Posted Content
Matching-CNN Meets KNN: Quasi-Parametric Human Parsing
Si Liu,Xiaodan Liang,Luoqi Liu,Xiaohui Shen,Jianchao Yang,Changsheng Xu,Liang Lin,Xiaochun Cao,Shuicheng Yan +8 more
TL;DR: This work aims to develop a new solution with the advantages of both methodologies, namely supervision from annotated data and the flexibility to use newly annotated (possibly uncommon) images, and present a quasi-parametric human parsing model.
Proceedings ArticleDOI
A Hybrid Neural Network for Chroma Intra Prediction
TL;DR: This paper proposes a chroma intra prediction method by exploiting both spatial and cross-channel correlations using a hybrid neural network, utilizing a convolutional neural network to extract features from the reconstructed luma samples of the current block, as well as utilize a fully connected network.