152 citations · 181 across the 11 of their papers we have counts for
11 papers
Decoupled Contrastive Multi-View Clustering with High-Order Random Walks
Yiding Lu, Yijie Lin, Mouxing Yang +3
In recent, some robust contrastive multi-view clustering (MvC) methods have been proposed, which construct data pairs from neighborhoods to alleviate the false negative issue, i.e.…
Noisy-Correspondence Learning for Text-to-Image Person Re-identification
Yang Qin, Yingke Chen, Dezhong Peng +3
Text-to-image person re-identification (TIReID) is a compelling topic in the cross-modal community, which aims to retrieve the target person based on a textual query. Although nume…
Correspondence-Free Domain Alignment for Unsupervised Cross-Domain Image Retrieval
Xu Wang, Dezhong Peng, Ming Yan +1
Cross-domain image retrieval aims at retrieving images across different domains to excavate cross-domain classificatory or correspondence relationships. This paper studies a less-t…
Incomplete Multi-view Clustering via Prototype-based Imputation
Haobin Li, Yunfan Li, Mouxing Yang +3
In this paper, we study how to achieve two characteristics highly-expected by incomplete multi-view clustering (IMvC). Namely, i) instance commonality refers to that within-cluster…
Twin Contrastive Learning for Online Clustering
Yunfan Li, Mouxing Yang, Dezhong Peng +3
This paper proposes to perform online clustering by conducting twin contrastive learning (TCL) at the instance and cluster level. Specifically, we find that when the data is projec…
Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric
Pengxin Zeng, Yunfan Li, Peng Hu +3
Fair clustering aims to divide data into distinct clusters while preventing sensitive attributes (\textit{e.g.}, gender, race, RNA sequencing technique) from dominating the cluster…
Adaptive Meta-learner via Gradient Similarity for Few-shot Text Classification
Tianyi Lei, Honghui Hu, Qiaoyang Luo +2
Few-shot text classification aims to classify the text under the few-shot scenario. Most of the previous methods adopt optimization-based meta learning to obtain task distribution.…
Contrastive Clustering
Yunfan Li, Peng Hu, Zitao Liu +3
In this paper, we propose a one-stage online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning. To…
Multiobjective Test Problems with Degenerate Pareto Fronts
Liangli Zhen, Miqing Li, Ran Cheng +2
In multiobjective optimisation, a set of scalable test problems with a variety of features allow researchers to investigate and evaluate the abilities of different optimisation alg…
Kernel Truncated Regression Representation for Robust Subspace Clustering
Liangli Zhen, Dezhong Peng, Wei Wang +1
Subspace clustering aims to group data points into multiple clusters of which each corresponds to one subspace. Most existing subspace clustering approaches assume that input data…
Locally linear representation for image clustering
Liangli Zhen, Zhang Yi, Xi Peng +1
It is a key to construct a similarity graph in graph-oriented subspace learning and clustering. In a similarity graph, each vertex denotes a data point and the edge weight represen…