Fine-grained Retrieval Prompt Tuning

• URL: http://arxiv.org/abs/2207.14465v1
• Date: Fri, 29 Jul 2022 04:10:04 GMT
• Title: Fine-grained Retrieval Prompt Tuning
• Authors: Shijie Wang, Jianlong Chang, Zhihui Wang, Haojie Li, Wanli Ouyang, Qi Tian
• Abstract summary: Fine-grained Retrieval Prompt Tuning steers a frozen pre-trained model to perform the fine-grained retrieval task from the perspectives of sample prompt and feature adaptation. Our FRPT with fewer learnable parameters achieves the state-of-the-art performance on three widely-used fine-grained datasets.
• Score: 149.9071858259279
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fine-grained object retrieval aims to learn discriminative representation to retrieve visually similar objects. However, existing top-performing works usually impose pairwise similarities on the semantic embedding spaces to continually fine-tune the entire model in limited-data regimes, thus resulting in easily converging to suboptimal solutions. In this paper, we develop Fine-grained Retrieval Prompt Tuning (FRPT), which steers a frozen pre-trained model to perform the fine-grained retrieval task from the perspectives of sample prompt and feature adaptation. Specifically, FRPT only needs to learn fewer parameters in the prompt and adaptation instead of fine-tuning the entire model, thus solving the convergence to suboptimal solutions caused by fine-tuning the entire model. Technically, as sample prompts, a structure perturbation prompt (SPP) is introduced to zoom and even exaggerate some pixels contributing to category prediction via a content-aware inhomogeneous sampling operation. In this way, SPP can make the fine-grained retrieval task aided by the perturbation prompts close to the solved task during the original pre-training. Besides, a category-specific awareness head is proposed and regarded as feature adaptation, which removes the species discrepancies in the features extracted by the pre-trained model using instance normalization, and thus makes the optimized features only include the discrepancies among subcategories. Extensive experiments demonstrate that our FRPT with fewer learnable parameters achieves the state-of-the-art performance on three widely-used fine-grained datasets.

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