Focus on What Matters: Enhancing Medical Vision-Language Models with Automatic Attention Alignment Tuning

Aofei Chang, Le Huang, Alex James Boyd, Parminder Bhatia, Taha A. Kass-Hout, Cao Xiao, Fenglong Ma

Abstract: Medical Large Vision-Language Models (Med-LVLMs) often exhibit suboptimal attention distribution on visual inputs, leading to hallucinated or inaccurate outputs. Existing methods primarily rely on inference-time interventions, which are limited in attention adaptation or require additional supervision. To address this, we propose A3Tune, a novel fine-tuning framework for Automatic Attention Alignment Tuning. ATune leverages zero-shot weak labels from SAM, refines them into prompt-aware labels using BioMedCLIP, and then selectively modifies visually-critical attention heads to improve alignment while minimizing interference. Additionally, we introduce a A3MoE module, enabling adaptive parameter selection for attention tuning across diverse prompts and images. Extensive experiments on medical VQA and report generation benchmarks show that A3Tune outperforms state-of-the-art baselines, achieving enhanced attention distributions and performance in Med-LVLMs.

External IDs:dblp:conf/acl/ChangHBBKXM25