1 citations · 1 across the 4 of their papers we have counts for
4 papers
Adaptive Confidence Multi-View Hashing for Multimedia Retrieval
Jian Zhu, Yu Cui, Zhangmin Huang +4
The multi-view hash method converts heterogeneous data from multiple views into binary hash codes, which is one of the critical technologies in multimedia retrieval. However, the c…
Central Similarity Multi-View Hashing for Multimedia Retrieval
Jian Zhu, Wen Cheng, Yu Cui +4
Hash representation learning of multi-view heterogeneous data is the key to improving the accuracy of multimedia retrieval. However, existing methods utilize local similarity and f…
Deep Metric Multi-View Hashing for Multimedia Retrieval
Jian Zhu, Zhangmin Huang, Xiaohu Ruan +3
Learning the hash representation of multi-view heterogeneous data is an important task in multimedia retrieval. However, existing methods fail to effectively fuse the multi-view fe…
AutoGMap: Learning to Map Large-scale Sparse Graphs on Memristive Crossbars
Bo Lyu, Shengbo Wang, Shiping Wen +4
The sparse representation of graphs has shown great potential for accelerating the computation of graph applications (e.g., Social Networks, Knowledge Graphs) on traditional comput…