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Fumin Shen
Researcher at University of Electronic Science and Technology of China
Publications - 244
Citations - 11134
Fumin Shen is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Hash function & Computer science. The author has an hindex of 44, co-authored 221 publications receiving 8028 citations. Previous affiliations of Fumin Shen include University of Adelaide & Nanjing University of Science and Technology.
Papers
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Proceedings ArticleDOI
Multi-view Latent Hashing for Efficient Multimedia Search
TL;DR: This paper proposes a novel unsupervised hashing approach, dubbed multi-view latent hashing (MVLH), to effectively incorporate multi- view data into hash code learning, and provides a novel scheme to directly learn the codes without resorting to continuous relaxations.
Proceedings ArticleDOI
Chat More: Deepening and Widening the Chatting Topic via A Deep Model
TL;DR: A novel deep scheme consisting of three channels, namely global, wide, and deep, which trains a Multi-layer Perceptron model to select some keywords for an in-depth discussion to generate desired responses in open-domain multi-turn dialog systems.
Proceedings Article
Compressed K -means for large-scale clustering
TL;DR: Extensive experimental results demonstrate that CKM outperforms the state-of-the-art large-scale clustering methods in terms of both computation and memory cost, while achieving comparable clustering accuracy.
Journal ArticleDOI
Towards Automatic Construction of Diverse, High-Quality Image Datasets
TL;DR: This work proposes a novel image dataset construction framework by employing multiple textual queries and formulate noisy textual queries removing and noisy images filtering as a multi-view and multi-instance learning problem separately.
Journal ArticleDOI
Temporal Reasoning Graph for Activity Recognition
TL;DR: This paper proposes an efficient temporal reasoning graph (TRG) to simultaneously capture the appearance features and temporal relation between video sequences at multiple time scales, and constructs learnable temporal relation graphs to explore temporal relation on the multi-scale range.