8 papers
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…
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…
Prompt Disentanglement via Language Guidance and Representation Alignment for Domain Generalization
De Cheng, Zhipeng Xu, Xinyang Jiang +3
Domain Generalization (DG) seeks to develop a versatile model capable of performing effectively on unseen target domains. Notably, recent advances in pre-trained Visual Foundation…
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…
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…
Exploring Interpretability for Visual Prompt Tuning with Cross-layer Concepts
Yubin Wang, Xinyang Jiang, De Cheng +4
Visual prompt tuning offers significant advantages for adapting pre-trained visual foundation models to specific tasks. However, current research provides limited insight into the…