Supervised Fine-tuning in turn Improves Visual Foundation Models

• URL: http://arxiv.org/abs/2401.10222v2
• Date: Thu, 11 Apr 2024 17:59:42 GMT
• Title: Supervised Fine-tuning in turn Improves Visual Foundation Models
• Authors: Xiaohu Jiang, Yixiao Ge, Yuying Ge, Dachuan Shi, Chun Yuan, Ying Shan,
• Abstract summary: Two-stage method ViSFT (Vision SFT) is proposed to unleash the fine-grained knowledge of vision foundation models. Vision transformer with over 4.4B parameters shows improvements across various out-of-domain benchmarks.
• Score: 74.1760864718129
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image-text training like CLIP has dominated the pretraining of vision foundation models in recent years. Subsequent efforts have been made to introduce region-level visual learning into CLIP's pretraining but face scalability challenges due to the lack of large-scale region-level datasets. Drawing inspiration from supervised fine-tuning (SFT) in natural language processing such as instruction tuning, we explore the potential of fine-grained SFT in enhancing the generation of vision foundation models after their pretraining. Thus a two-stage method ViSFT (Vision SFT) is proposed to unleash the fine-grained knowledge of vision foundation models. In ViSFT, the vision foundation model is enhanced by performing visual joint learning on some in-domain tasks and then tested on out-of-domain benchmarks. With updating using ViSFT on 8 V100 GPUs in less than 2 days, a vision transformer with over 4.4B parameters shows improvements across various out-of-domain benchmarks including vision and vision-linguistic scenarios.

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