F
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.
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Proceedings ArticleDOI
Supervised Discrete Hashing
TL;DR: This work proposes a new supervised hashing framework, where the learning objective is to generate the optimal binary hash codes for linear classification, and introduces an auxiliary variable to reformulate the objective such that it can be solved substantially efficiently by employing a regularization algorithm.
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Supervised Discrete Hashing
TL;DR: Supervised Discrete Hashing (SDH) as mentioned in this paper proposes a new supervised hashing framework, where the learning objective is to generate the optimal binary hash codes for linear classification, which can support efficient storage and retrieval for high-dimensional data such as images, videos, documents, etc.
Journal ArticleDOI
Learning Discriminative Binary Codes for Large-scale Cross-modal Retrieval
TL;DR: A novel cross- modal hashing method, termed discrete cross-modal hashing (DCH), which directly learns discriminative binary codes while retaining the discrete constraints, and an effective discrete optimization algorithm is developed for DCH to jointly learn the modality-specific hash function and the unified binary codes.
Journal ArticleDOI
Unsupervised Deep Hashing with Similarity-Adaptive and Discrete Optimization
TL;DR: This work proposes a simple yet effective unsupervised hashing framework, named Similarity-Adaptive Deep Hashing (SADH), which alternatingly proceeds over three training modules: deep hash model training, similarity graph updating and binary code optimization.
Journal ArticleDOI
Binary Multi-View Clustering
TL;DR: A novel Binary Multi-View Clustering (BMVC) framework, which can dexterously manipulate multi-view image data and easily scale to large data, and is formulated by two key components: compact collaborative discrete representation learning and binary clustering structure learning, in a joint learning framework.