220 citations · 351 across the 21 of their papers we have counts for
7 papers · 1 filter
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…
Improved Diversity-Promoting Collaborative Metric Learning for Recommendation
Shilong Bao, Qianqian Xu, Zhiyong Yang +3
Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and collaborative filtering. Fo…
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…
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…