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cs.CV2026

Staying VIGILant: Mitigating Visual Laziness via Counterfactual Visual Alignment in MLLMs

Xi Xiao, Chen Liu, Chih-Ting Liao +9

Multimodal large language models (MLLMs) extend large language models (LLMs) with visual perception, enabling joint reasoning over images and text. Despite inheriting strong reason…

cs.CV2025

Doctor Approved: Generating Medically Accurate Skin Disease Images through AI-Expert Feedback

Janet Wang, Yunbei Zhang, Zhengming Ding +1

Paucity of medical data severely limits the generalizability of diagnostic ML models, as the full spectrum of disease variability can not be represented by a small clinical dataset…

cs.CV2025

Describe Anything in Medical Images

Xi Xiao, Yunbei Zhang, Thanh-Huy Nguyen +10

Localized image captioning has made significant progress with models like the Describe Anything Model (DAM), which can generate detailed region-specific descriptions without explic…

cs.CV2025

Enhancing Skin Disease Diagnosis: Interpretable Visual Concept Discovery with SAM

Xin Hu, Janet Wang, Jihun Hamm +2

Current AI-assisted skin image diagnosis has achieved dermatologist-level performance in classifying skin cancer, driven by rapid advancements in deep learning architectures. Howev…

cs.CV2024

From Majority to Minority: A Diffusion-based Augmentation for Underrepresented Groups in Skin Lesion Analysis

Janet Wang, Yunsung Chung, Zhengming Ding +1

AI-based diagnoses have demonstrated dermatologist-level performance in classifying skin cancer. However, such systems are prone to under-performing when tested on data from minori…

cs.CV2024

Achieving Reliable and Fair Skin Lesion Diagnosis via Unsupervised Domain Adaptation

Janet Wang, Yunbei Zhang, Zhengming Ding +1

The development of reliable and fair diagnostic systems is often constrained by the scarcity of labeled data. To address this challenge, our work explores the feasibility of unsupe…