From Generalist to Specialist: Adapting Vision Language Models via Task-Specific Visual Instruction Tuning

ICLR 2025 Conference Submission1301 Authors

Keywords: Vison Language Models, Task Specific Models, Visual Instruction Tuning

TL;DR: We proposed a task-specific visual instruction framework to adapt VLMs for specific tasks with task-specific model-level performance.

Abstract: 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.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 1301