3 accepted papers
Trustworthy multi-modal learning integrates multiple sources of data reliably. However, the current methods still focus on performance improvement by developing deep multi-modal networks. These approaches frequently encounter challenges due to the inherent non-convex nature of deep neural networks a…
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 recent years. However, there is an untrusted fusion problem. The reasons for this problem are as follows: 1) The current me…
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 current methods mainly explore the complementarity among multiple views while lacking confidence in learning and fusion.…