5 papers
Active-Passive Federated Learning for Vertically Partitioned Multi-view Data
Jiyuan Liu, Siqi Wang, Xinhang Wan +4
Vertical federated learning is a natural and elegant approach to integrate multi-view data vertically partitioned across devices (clients) while preserving their privacies. Apart f…
Object Affordance Recognition and Grounding via Multi-scale Cross-modal Representation Learning
Xinhang Wan, Dongqiang Gou, Xinwang Liu +2
A core problem of Embodied AI is to learn object manipulation from observation, as humans do. To achieve this, it is important to localize 3D object affordance areas through observ…
Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiation
Yaowen Hu, Wenxuan Tu, Yue Liu +4
Deep graph clustering (DGC), which aims to unsupervisedly separate the nodes in an attribute graph into different clusters, has seen substantial potential in various industrial sce…
Intra-view and Inter-view Correlation Guided Multi-view Novel Class Discovery
Xinhang Wan, Jiyuan Liu, Qian Qu +6
In this paper, we address the problem of novel class discovery (NCD), which aims to cluster novel classes by leveraging knowledge from disjoint known classes. While recent advances…
Incomplete Multi-view Clustering via Diffusion Contrastive Generation
Yuanyang Zhang, Yijie Lin, Weiqing Yan +6
Incomplete multi-view clustering (IMVC) has garnered increasing attention in recent years due to the common issue of missing data in multi-view datasets. The primary approach to ad…