220 citations · 351 across the 21 of their papers we have counts for
11 papers · 1 filter
Focal-SAM: Focal Sharpness-Aware Minimization for Long-Tailed Classification
Sicong Li, Qianqian Xu, Zhiyong Yang +4
Real-world datasets often follow a long-tailed distribution, making generalization to tail classes difficult. Recent methods resorted to long-tail variants of Sharpness-Aware Minim…
Top-K Pairwise Ranking: Bridging the Gap Among Ranking-Based Measures for Multi-Label Classification
Zitai Wang, Qianqian Xu, Zhiyong Yang +4
Multi-label ranking, which returns multiple top-ranked labels for each instance, has a wide range of applications for visual tasks. Due to its complicated setting, prior arts have…
DirMixE: Harnessing Test Agnostic Long-tail Recognition with Hierarchical Label Variations
Zhiyong Yang, Qianqian Xu, Sicong Li +3
This paper explores test-agnostic long-tail recognition, a challenging long-tail task where the test label distributions are unknown and arbitrarily imbalanced. We argue that the v…
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…
A Unified Perspective for Loss-Oriented Imbalanced Learning via Localization
Zitai Wang, Qianqian Xu, Zhiyong Yang +4
Due to the inherent imbalance in real-world datasets, naïve Empirical Risk Minimization (ERM) tends to bias the learning process towards the majority classes, hindering generalizat…
LSGNN: Towards General Graph Neural Network in Node Classification by Local Similarity
Yuhan Chen, Yihong Luo, Jing Tang +4
Heterophily has been considered as an issue that hurts the performance of Graph Neural Networks (GNNs). To address this issue, some existing work uses a graph-level weighted fusion…
OpenAUC: Towards AUC-Oriented Open-Set Recognition
Zitai Wang, Qianqian Xu, Zhiyong Yang +3
Traditional machine learning follows a close-set assumption that the training and test set share the same label space. While in many practical scenarios, it is inevitable that some…
MaxMatch: Semi-Supervised Learning with Worst-Case Consistency
Yangbangyan Jiang, Xiaodan Li, Yuefeng Chen +5
In recent years, great progress has been made to incorporate unlabeled data to overcome the inefficiently supervised problem via semi-supervised learning (SSL). Most state-of-the-a…
A Tale of HodgeRank and Spectral Method: Target Attack Against Rank Aggregation Is the Fixed Point of Adversarial Game
Ke Ma, Qianqian Xu, Jinshan Zeng +3
Rank aggregation with pairwise comparisons has shown promising results in elections, sports competitions, recommendations, and information retrieval. However, little attention has…
Optimizing Partial Area Under the Top-k Curve: Theory and Practice
Zitai Wang, Qianqian Xu, Zhiyong Yang +3
Top-k error has become a popular metric for large-scale classification benchmarks due to the inevitable semantic ambiguity among classes. Existing literature on top-k optimization…
Geometry Interaction Knowledge Graph Embeddings
Zongsheng Cao, Qianqian Xu, Zhiyong Yang +2
Knowledge graph (KG) embeddings have shown great power in learning representations of entities and relations for link prediction tasks. Previous work usually embeds KGs into a sing…