9 citations · 27 across the 20 of their papers we have counts for
6 papers · 1 filter
EDGE: Unknown-aware Multi-label Learning by Energy Distribution Gap Expansion
Yuchen Sun, Qianqian Xu, Zitai Wang +2
Multi-label Out-Of-Distribution (OOD) detection aims to discriminate the OOD samples from the multi-label In-Distribution (ID) ones. Compared with its multiclass counterpart, it is…
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…
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…
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…