Fully Fine-Tuning Beats Parameter Efficient Fine-Tuning for CLIP in Data-Limited Scenarios

Mushui Liu, Bozheng Li, Ziqian Lu, Jun Dan, Yunlong Yu

Keywords: Vision-Language Models; Parameter-Efficient Fine-tuning;

Abstract: Prompt tuning, which involves training a small set of parameters, effectively enhances the pre-trained Vision-Language Models (VLMs) to downstream tasks. However, they often come at the cost of flexibility and adaptability when the tuned models are applied to different datasets or domains. In this paper, we revisit the vanilla full fine-tuning in VLMs and show that fully fine-tuning can be more efficient than prompt tuning in data-limited scenarios. To mitigate the overfitting and catastrophic forgetting issues encountered when fine-tuning the entire VLMs for specific tasks under limited supervision, we propose a framework named CLIP-CITE via designing a discriminative visual-text task, further aligning the visual-text semantics in a supervision manner, and integrating knowledge distillation techniques to preserve the gained knowledge. Extensive experimental results under few-shot learning, base-to-new generalization, domain generalization, and cross-domain generalization settings, demonstrate that our method effectively enhances the performance on specific tasks under limited supervision while preserving the versatility of the VLMs on other datasets.

Primary Area: transfer learning, meta learning, and lifelong learning

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Submission Number: 10457