Publications (7)
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…
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…
SOLAR-RL: Semi-Online Long-horizon Assignment Reinforcement Learning
Jichao Wang, Liuyang Bian, Yufeng Zhou +9
As Multimodal Large Language Models (MLLMs) mature, GUI agents are evolving from static interactions to complex navigation. While Reinforcement Learning (RL) has emerged as a promi…
Time-efficient Garbage Collection in SSDs
Lars Nagel, Tim SüÃ, Kevin Kremer +3
SSDs are currently replacing magnetic disks in many application areas. A challenge of the underlying flash technology is that data cannot be updated in-place. A block consisting of…
MoEGCL: Mixture of Ego-Graphs Contrastive Representation Learning for Multi-View Clustering
Jian Zhu, Xin Zou, Jun Sun +7
In recent years, the advancement of Graph Neural Networks (GNNs) has significantly propelled progress in Multi-View Clustering (MVC). However, existing methods face the problem of…
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…
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…