39 citations · 91 across the 11 of their papers we have counts for
12 papers
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…
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…
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…
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…
Contrastive Continual Multi-view Clustering with Filtered Structural Fusion
Xinhang Wan, Jiyuan Liu, Hao Yu +5
Multi-view clustering thrives in applications where views are collected in advance by extracting consistent and complementary information among views. However, it overlooks scenari…
Efficient Multi-View Graph Clustering with Local and Global Structure Preservation
Yi Wen, Suyuan Liu, Xinhang Wan +5
Anchor-based multi-view graph clustering (AMVGC) has received abundant attention owing to its high efficiency and the capability to capture complementary structural information acr…
Scalable Incomplete Multi-View Clustering with Structure Alignment
Yi Wen, Siwei Wang, Ke Liang +6
The success of existing multi-view clustering (MVC) relies on the assumption that all views are complete. However, samples are usually partially available due to data corruption or…
Unpaired Multi-View Graph Clustering with Cross-View Structure Matching
Yi Wen, Siwei Wang, Qing Liao +4
Multi-view clustering (MVC), which effectively fuses information from multiple views for better performance, has received increasing attention. Most existing MVC methods assume tha…
One-step Multi-view Clustering with Diverse Representation
Xinhang Wan, Jiyuan Liu, Xinwang Liu +5
Multi-view clustering has attracted broad attention due to its capacity to utilize consistent and complementary information among views. Although tremendous progress has been made…
Fast Continual Multi-View Clustering with Incomplete Views
Xinhang Wan, Bin Xiao, Xinwang Liu +3
Multi-view clustering (MVC) has gained broad attention owing to its capacity to exploit consistent and complementary information across views. This paper focuses on a challenging i…
Auto-weighted Multi-view Clustering for Large-scale Data
Xinhang Wan, Xinwang Liu, Jiyuan Liu +6
Multi-view clustering has gained broad attention owing to its capacity to exploit complementary information across multiple data views. Although existing methods demonstrate deligh…