1 citations · 2 across the 6 of their papers we have counts for
6 papers
Trusted Mamba Contrastive Network for Multi-View Clustering
Jian Zhu, Xin Zou, Lei Liu +4
Multi-view clustering can partition data samples into their categories by learning a consensus representation in an unsupervised way and has received more and more attention in rec…
CLIP Multi-modal Hashing for Multimedia Retrieval
Jian Zhu, Mingkai Sheng, Zhangmin Huang +5
Multi-modal hashing methods are widely used in multimedia retrieval, which can fuse multi-source data to generate binary hash code. However, the individual backbone networks have l…
Deep Mamba Multi-modal Learning
Jian Zhu, Xin Zou, Yu Cui +3
Inspired by the excellent performance of Mamba networks, we propose a novel Deep Mamba Multi-modal Learning (DMML). It can be used to achieve the fusion of multi-modal features. We…
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…
CLIP Multi-modal Hashing: A new baseline CLIPMH
Jian Zhu, Mingkai Sheng, Mingda Ke +2
The multi-modal hashing method is widely used in multimedia retrieval. It can fuse multi-source data to generate binary hash code. However, the current multi-modal methods have the…
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…