4 citations · 8 across the 13 of their papers we have counts for
6 papers · 1 filter
Mind the Way You Select Negative Texts: Pursuing the Distance Consistency in OOD Detection with VLMs
Zhikang Xu, Qianqian Xu, Zitai Wang +4
Out-of-distribution (OOD) detection seeks to identify samples from unknown classes, a critical capability for deploying machine learning models in open-world scenarios. Recent rese…
Making Training-Free Diffusion Segmentors Scale with the Generative Power
Benyuan Meng, Qianqian Xu, Zitai Wang +3
As powerful generative models, text-to-image diffusion models have recently been explored for discriminative tasks. A line of research focuses on adapting a pre-trained diffusion m…
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…
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…