Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learning

• URL: http://arxiv.org/abs/2402.02340v2
• Date: Thu, 14 Mar 2024 23:29:47 GMT
• Title: Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learning
• Authors: Li Ren, Chen Chen, Liqiang Wang, Kien Hua,
• Abstract summary: Existing solutions concentrate on fine-tuning the pre-trained models on conventional image datasets. We propose a novel and effective framework based on learning Visual Prompts (VPT) in the pre-trained Vision Transformers (ViT) We demonstrate that our new approximations with semantic information are superior to representative capabilities.
• Score: 13.964106147449051
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep Metric Learning (DML) has long attracted the attention of the machine learning community as a key objective. Existing solutions concentrate on fine-tuning the pre-trained models on conventional image datasets. As a result of the success of recent pre-trained models trained from larger-scale datasets, it is challenging to adapt the model to the DML tasks in the local data domain while retaining the previously gained knowledge. In this paper, we investigate parameter-efficient methods for fine-tuning the pre-trained model for DML tasks. In particular, we propose a novel and effective framework based on learning Visual Prompts (VPT) in the pre-trained Vision Transformers (ViT). Based on the conventional proxy-based DML paradigm, we augment the proxy by incorporating the semantic information from the input image and the ViT, in which we optimize the visual prompts for each class. We demonstrate that our new approximations with semantic information are superior to representative capabilities, thereby improving metric learning performance. We conduct extensive experiments to demonstrate that our proposed framework is effective and efficient by evaluating popular DML benchmarks. In particular, we demonstrate that our fine-tuning method achieves comparable or even better performance than recent state-of-the-art full fine-tuning works of DML while tuning only a small percentage of total parameters.

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