Retrieval-Enhanced Visual Prompt Learning for Few-shot Classification

• URL: http://arxiv.org/abs/2306.02243v3
• Date: Thu, 21 Nov 2024 04:12:53 GMT
• Title: Retrieval-Enhanced Visual Prompt Learning for Few-shot Classification
• Authors: Jintao Rong, Hao Chen, Linlin Ou, Tianxiao Chen, Xinyi Yu, Yifan Liu,
• Abstract summary: We propose retrieval-enhanced visual prompt learning (RePrompt) to cache and reuse knowledge of downstream tasks. During inference, our enhanced model can reference similar samples brought by retrieval to make more accurate predictions. RePrompt attains state-of-the-art performance on a wide range of vision datasets.
• Score: 9.843214426749764
• License:
• Abstract: The Contrastive Language-Image Pretraining (CLIP) model has been widely used in various downstream vision tasks. The few-shot learning paradigm has been widely adopted to augment its capacity for these tasks. However, current paradigms may struggle with fine-grained classification, such as satellite image recognition, due to widening domain gaps. To address this limitation, we propose retrieval-enhanced visual prompt learning (RePrompt), which introduces retrieval mechanisms to cache and reuse the knowledge of downstream tasks. RePrompt constructs a retrieval database from either training examples or external data if available, and uses a retrieval mechanism to enhance multiple stages of a simple prompt learning baseline, thus narrowing the domain gap. During inference, our enhanced model can reference similar samples brought by retrieval to make more accurate predictions. A detailed analysis reveals that retrieval helps to improve the distribution of late features, thus, improving generalization for downstream tasks. Reprompt attains state-of-the-art performance on a wide range of vision datasets, including 11 image datasets, 3 video datasets, 1 multi-view dataset, and 4 domain generalization benchmarks.

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