activity
20142024
most citedRegularized Contrastive Partial Multi-view Outlier Detection

7 citations · 16 across the 7 of their papers we have counts for

collaborators

7 papers

cs.MM20247 cited

Regularized Contrastive Partial Multi-view Outlier Detection

Yijia Wang, Qianqian Xu, Yangbangyan Jiang +2

In recent years, multi-view outlier detection (MVOD) methods have advanced significantly, aiming to identify outliers within multi-view datasets. A key point is to better detect cl…

cs.LG20244 cited

HGOE: Hybrid External and Internal Graph Outlier Exposure for Graph Out-of-Distribution Detection

Junwei He, Qianqian Xu, Yangbangyan Jiang +3

With the progressive advancements in deep graph learning, out-of-distribution (OOD) detection for graph data has emerged as a critical challenge. While the efficacy of auxiliary da…

cs.CV2024

Not All Pairs are Equal: Hierarchical Learning for Average-Precision-Oriented Video Retrieval

Yang Liu, Qianqian Xu, Peisong Wen +2

The rapid growth of online video resources has significantly promoted the development of video retrieval methods. As a standard evaluation metric for video retrieval, Average Preci…

cs.AI20245 cited

Sequential Manipulation Against Rank Aggregation: Theory and Algorithm

Ke Ma, Qianqian Xu, Jinshan Zeng +4

Rank aggregation with pairwise comparisons is widely encountered in sociology, politics, economics, psychology, sports, etc . Given the enormous social impact and the consequent in…

cs.LG2023

DRAUC: An Instance-wise Distributionally Robust AUC Optimization Framework

Siran Dai, Qianqian Xu, Zhiyong Yang +2

The Area Under the ROC Curve (AUC) is a widely employed metric in long-tailed classification scenarios. Nevertheless, most existing methods primarily assume that training and testi…

cs.CV2023

When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top- Multi-Label Learning

Yuchen Sun, Qianqian Xu, Zitai Wang +1

With the great success of deep neural networks, adversarial learning has received widespread attention in various studies, ranging from multi-class learning to multi-label learning…