activity
20232026
most citedLLM-RadJudge: Achieving Radiologist-Level Evaluation for X-Ray Report Generation

5 citations · 8 across the 18 of their papers we have counts for

collaborators
Showing 2026Show all

6 papers · 1 filter

cs.CV2026

PAPT++: Risk-Aware Adversarial Tuning and Generation for Single Domain Generalization

Zhipeng Xu, De Cheng, Xinyang Jiang +5

Single domain generalization (SDG) aims to learn a model from one labeled source domain that generalizes to unseen target domains. A common strategy is to enrich the source distrib…

cs.CV2026

Beyond Text Conditioning: A Systematic Study of MLLM-DiT Fusion for Video Generation

Yanbo Ding, Yijia Fan, Caihua Shan +8

Diffusion Transformers (DiTs) have become the dominant paradigm for high-fidelity video generation, yet their ability to perform high-level semantic planning remains limited. While…

cs.CV2026

Dual-Branch Cross-Projection Debiasing through Diffusion-based Disentanglement

Xiangqian Zhao, Xinyang Jiang, Zhipeng Xu +5

Foundation models trained on biased datasets often rely on spurious correlations between target labels and non-causal attributes, resulting in poor generalization on minority group…

cs.CV2026

Adversarial Domain Prompt Tuning and Generation for Single Domain Generalization

Zhipeng Xu, De Cheng, Xinyang Jiang +3

Single domain generalization (SDG) aims to learn a robust model, which could perform well on many unseen domains while there is only one single domain available for training. One o…

cs.AI2026

MedCUA-Bench: A Screenshot-Only Benchmark for Clinical Computer-Use Agents

Jia Yu, Zilong Wang, Xinyang Jiang +2

Computer-use agents could automate repetitive screen-based clinical work, but their reliability in medical graphical user interfaces remains largely unvalidated. Existing benchmark…

cs.AI2026

Reasoning-Driven Multimodal LLM for Domain Generalization

Zhipeng Xu, Zilong Wang, Xinyang Jiang +3

This paper addresses the domain generalization (DG) problem in deep learning. While most DG methods focus on enforcing visual feature invariance, we leverage the reasoning capabili…