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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Book ChapterDOI
Efficient highly over-complete sparse coding using a mixture model
TL;DR: A mixture sparse coding model that can produce high-dimensional sparse representations very efficiently that works pretty well with linear classifiers and effectively encourages data that are similar to each other to enjoy similar sparse representations is described.
Proceedings Article
Building a large scale dataset for image emotion recognition: the fine print and the benchmark
TL;DR: In this paper, the authors introduce a new data set, which started from 3+ million weakly labeled images of different emotions and ended up 30 times as large as the current largest publicly available visual emotion data set.
Book ChapterDOI
Non-local kernel regression for image and video restoration
TL;DR: Extensive experimental results on both single images and realistic video sequences demonstrate the superiority of the proposed framework for SR tasks over previous works both qualitatively and quantitatively.
Posted Content
YouTube-VOS: Sequence-to-Sequence Video Object Segmentation
Ning Xu,Linjie Yang,Yuchen Fan,Jianchao Yang,Dingcheng Yue,Yuchen Liang,Brian Price,Scott Cohen,Thomas S. Huang +8 more
TL;DR: This work builds a new large-scale video object segmentation dataset called YouTube Video Object Segmentation dataset (YouTube-VOS) and proposes a novel sequence-to-sequence network to fully exploit long-term spatial-temporal information in videos for segmentation.
Proceedings ArticleDOI
DeepFont: Identify Your Font from An Image
Zhangyang Wang,Jianchao Yang,Hailin Jin,Eli Shechtman,Aseem Agarwala,Jonathan Brandt,Thomas S. Huang +6 more
TL;DR: This work builds up the first available large-scale VFR dataset, named AdobeVFR, consisting of both labeled synthetic data and partially labeled real-world data, and introduces a Convolutional Neural Network decomposition approach and a novel learning-based model compression approach in order to reduce the DeepFont model size without sacrificing its performance.