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arXiv · 2305.07019

Musketeer: Joint Training for Multi-task Vision Language Model with Task Explanation Prompts

Zhaoyang Zhang·Yantao Shen·Kunyu Shi·Zhaowei Cai·Jun Fang·Siqi Deng·Hao Yang·Davide Modolo·Zhuowen Tu·Stefano Soatto
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Abstract

We present a vision-language model whose parameters are jointly trained on all tasks and fully shared among multiple heterogeneous tasks which may interfere with each other, resulting in a single model which we named Musketeer. The integration of knowledge across heterogeneous tasks is enabled by a novel feature called Task Explanation Prompt (TEP). With rich and structured information such as task input/output format, TEP reduces interference among tasks, allowing the model to focus on their shared structure. With a single model, Musketeer achieves results comparable to or better than strong baselines trained on single tasks, almost uniformly across multiple tasks.

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Zhaoyang Zhang, Yantao Shen, Kunyu Shi, Zhaowei Cai, Jun Fang, Siqi Deng, Hao Yang, Davide Modolo, Zhuowen Tu, Stefano Soatto. 2024-03-14. Musketeer: Joint Training for Multi-task Vision Language Model with Task Explanation Prompts. https://arxiv.org/abs/2305.07019

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