H
Hongyuan Zhu
Researcher at Agency for Science, Technology and Research
Publications - 66
Citations - 2588
Hongyuan Zhu is an academic researcher from Agency for Science, Technology and Research. The author has contributed to research in topics: Computer science & Cluster analysis. The author has an hindex of 20, co-authored 56 publications receiving 1532 citations. Previous affiliations of Hongyuan Zhu include Nanyang Technological University & Institute for Infocomm Research Singapore.
Papers
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Journal ArticleDOI
AnomalyNet: An Anomaly Detection Network for Video Surveillance
TL;DR: This paper proposes a new neural network for anomaly detection by deeply achieving feature learning, sparse representation, and dictionary learning in three joint neural processing blocks by proposing an adaptive iterative hard-thresholding algorithm (adaptive ISTA) and reformulating the adaptive ISTA as a new long short-term memory (LSTM).
Journal ArticleDOI
Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation
TL;DR: A comprehensive review of the recent progress in image segmentation, covering 190 publications, gives an overview of broad segmentation topics including not only the classic unsupervised methods, but also the recent weakly-/semi- supervised methods and the fully-super supervised methods.
Posted Content
Beyond Pixels: A Comprehensive Survey from Bottom-up to Semantic Image Segmentation and Cosegmentation
TL;DR: A comprehensive review of the recent progress in image segmentation can be found in this article, where the authors give an overview of broad areas of segmentation topics including not only the classic bottom-up approaches, but also the recent development in superpixel, interactive methods, object proposals, semantic image parsing and image cosegmentation.
Journal ArticleDOI
Partition level multiview subspace clustering.
Zhao Kang,Xinjia Zhao,Chong Peng,Hongyuan Zhu,Joey Tianyi Zhou,Xi Peng,Wenyu Chen,Zenglin Xu +7 more
TL;DR: A unified multiview subspace clustering model is proposed which incorporates the graph learning from each view, the generation of basic partitions, and the fusion of consensus partition which is seamlessly integrated and can be iteratively boosted by each other towards an overall optimal solution.
Proceedings ArticleDOI
Spatial Fusion GAN for Image Synthesis
TL;DR: Qualitative and quantitative comparisons with the state-of-the-art demonstrate the superiority of the proposed Spatial Fusion GAN (SF-GAN), which combines a geometry synthesizer and an appearance synthesizer to achieve synthesis realism in both geometry and appearance spaces.