From Generalist to Specialist: Adapting Vision Language Models via
  Task-Specific Visual Instruction Tuning
Authors: name: Yang Bai, name: Yang Zhou, name: Jun Zhou, name: Rick Siow Mong Goh, name: Daniel Shu Wei Ting, name: Yong Liu

Large vision language models (VLMs) combine large language models with vision
encoders, demonstrating promise across various tasks. However, they often
underperform in task-specific applications due to domain gaps between
pre-training and fine-tuning. We introduce VITask, a novel framework that
enhances task-specific adaptability of VLMs by integrating task-specific models
(TSMs). VITask employs three key strategies: exemplar prompting (EP), response
distribution alignment (RDA), and contrastive response tuning (CRT) to improve
the task-specific performance of VLMs by adjusting their response
distributions. EP allows TSM features to guide VLMs, while RDA enables VLMs to
adapt without TSMs during inference by learning from exemplar-prompted models.
CRT further optimizes the ranking of correct image-response pairs, thereby
reducing the risk of generating undesired responses. Experiments on 12 medical
diagnosis datasets across 9 imaging modalities show that VITask outperforms
both vanilla instruction-tuned VLMs and TSMs, showcasing its ability to
integrate complementary features from both models effectively. Additionally,
VITask offers practical advantages such as flexible TSM integration and
robustness to incomplete instructions, making it a versatile and efficient
solution for task-specific VLM tuning. Our code are available at
https://github.com/baiyang4/VITask.