activity
20162026
most citedMaxMatch: Semi-Supervised Learning with Worst-Case Consistency

34 citations · 173 across the 63 of their papers we have counts for

collaborators

61 papers

cs.CV2026

UniTraffic-Agent: Unified Traffic Video Reasoning for AI City Challenge 2026 Track 3 with Two Out-of-Domain Evaluations

Peng Li, Qianqian Xu, Shilong Bao +2

Traffic video understanding has become an important problem in intelligent transportation, as road videos provide direct evidence for accidents, violations, and interactions betwee…

cs.CR2026

GoodDiffusion: Proactive Copyright Protection for Diffusion Bridge Models via Learnable Sample-specific Signatures

Shixi Qin, Zhiyong Yang, Shilong Bao +3

This paper tackles the challenging problem of developing a proactive copyright protection mechanism that cuts off unauthorized use of diffusion bridge models. Existing studies larg…

cs.CV2026

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…

cs.CV2026

From Static to Dynamic: Exploring Self-supervised Image-to-Video Representation Transfer Learning

Yang Liu, Qianqian Xu, Peisong Wen +3

Recent studies have made notable progress in video representation learning by transferring image-pretrained models to video tasks, typically with complex temporal modules and video…

cs.CV2026

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…

cs.CV2026

Guiding Diffusion-based Reconstruction with Contrastive Signals for Balanced Visual Representation

Boyu Han, Qianqian Xu, Shilong Bao +4

The limited understanding capacity of the visual encoder in Contrastive Language-Image Pre-training (CLIP) has become a key bottleneck for downstream performance. This capacity inc…