Multi-Modal Proxy Learning Towards Personalized Visual Multiple Clustering

arXiv:2404.15655v1 Announce Type: new
Abstract: Multiple clustering has gained significant attention in recent years due to its potential to reveal multiple hidden structures of data from different perspectives. The advent of deep multiple clustering techniques has notably advanced the performance by uncovering complex patterns and relationships within large datasets. However, a major challenge arises as users often do not need all the clusterings that algorithms generate, and figuring out the one needed requires a substantial understanding of each clustering result. …

arxiv clustering cs.cv modal multi-modal multiple personalized type visual