4 citations · 9 across the 14 of their papers we have counts for
8 papers · 1 filter
VLMProbe: An Open-source Library for VLM Probing and Interpretation
Gexin Huang, Haisheng Fu, Yanting Yang +4
Understanding how vision-language models use visual information requires connecting internal representations to model behavior. We present vlmprobe, an open-source library that int…
VisualCoding: Learning Executable Skills for Reliable Visual Reasoning
Jiaqi Liu, Liu Jiaqi, Ruinan Jin +3
Learning reusable skills can improve visual reasoning agents, yet applying a skill still requires the agent to implement or invoke a procedure and produce the final answer. Errors…
Verification Mirage: Mapping the Reliability Boundary of Self-Verification in Medical VQA
Ruinan Jin, Beidi Zhao, Myeongkyun Kang +2
Self-verification, re-invoking the same vision language model (VLM) in a fresh context to check its own generated answer, is increasingly used as a default safety layer for medical…
DUCX: Decomposing Unfairness in Tool-Using Chest X-ray Agents
Zikang Xu, Ruinan Jin, Xiaoxiao Li
Fairness in medical agents is becoming critical as tool-using clinical AI systems orchestrate specialized vision and language modules for tasks such as chest X-ray question answeri…
See-in-Pairs: Reference Image-Guided Comparative Vision-Language Models for Medical Diagnosis
Ruinan Jin, Gexin Huang, Xinwei Shen +3
Medical image diagnosis is challenging because many diseases resemble normal anatomy and exhibit substantial interpatient variability. Clinicians routinely rely on comparative diag…
FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Models
Ruinan Jin, Zikang Xu, Yuan Zhong +4
The advent of foundation models (FMs) in healthcare offers unprecedented opportunities to enhance medical diagnostics through automated classification and segmentation tasks. Howev…
Debiased Noise Editing on Foundation Models for Fair Medical Image Classification
Ruinan Jin, Wenlong Deng, Minghui Chen +1
In the era of Foundation Models' (FMs) rising prominence in AI, our study addresses the challenge of biases in medical images while the model operates in black-box (e.g., using FM…
Backdoor Attack on Unpaired Medical Image-Text Foundation Models: A Pilot Study on MedCLIP
Ruinan Jin, Chun-Yin Huang, Chenyu You +1
In recent years, foundation models (FMs) have solidified their role as cornerstone advancements in the deep learning domain. By extracting intricate patterns from vast datasets, th…