7 citations · 25 across the 49 of their papers we have counts for
13 papers · 1 filter
SSE-SAM: Balancing Head and Tail Classes Gradually through Stage-Wise SAM
Xingyu Lyu, Qianqian Xu, Zhiyong Yang +2
Real-world datasets often exhibit a long-tailed distribution, where vast majority of classes known as tail classes have only few samples. Traditional methods tend to overfit on the…
Bidirectional Logits Tree: Pursuing Granularity Reconcilement in Fine-Grained Classification
Zhiguang Lu, Qianqian Xu, Shilong Bao +2
This paper addresses the challenge of Granularity Competition in fine-grained classification tasks, which arises due to the semantic gap between multi-granularity labels. Existing…
Suppress Content Shift: Better Diffusion Features via Off-the-Shelf Generation Techniques
Benyuan Meng, Qianqian Xu, Zitai Wang +3
Diffusion models are powerful generative models, and this capability can also be applied to discrimination. The inner activations of a pre-trained diffusion model can serve as feat…
Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features
Benyuan Meng, Qianqian Xu, Zitai Wang +2
Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense fea…
AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation
Boyu Han, Qianqian Xu, Zhiyong Yang +4
The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have bee…
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…
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…
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…
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…
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…
Size-invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection
Feiran Li, Qianqian Xu, Shilong Bao +4
This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We ob…
ReconBoost: Boosting Can Achieve Modality Reconcilement
Cong Hua, Qianqian Xu, Shilong Bao +2
This paper explores a novel multi-modal alternating learning paradigm pursuing a reconciliation between the exploitation of uni-modal features and the exploration of cross-modal in…
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…