Few-Shot Recognition via Stage-Wise Retrieval-Augmented Finetuning

• URL: http://arxiv.org/abs/2406.11148v3
• Date: Fri, 21 Mar 2025 20:56:08 GMT
• Title: Few-Shot Recognition via Stage-Wise Retrieval-Augmented Finetuning
• Authors: Tian Liu, Huixin Zhang, Shubham Parashar, Shu Kong,
• Abstract summary: Few-shot recognition aims to train a classification model with only a few labeled examples of each concept concerned by a downstream task.<n>We develop methods to solve FSR by leveraging a pretrained Vision-Language Model (VLM)
• Score: 8.348143234047486
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Few-shot recognition (FSR) aims to train a classification model with only a few labeled examples of each concept concerned by a downstream task, where data annotation cost can be prohibitively high. We develop methods to solve FSR by leveraging a pretrained Vision-Language Model (VLM). We particularly explore retrieval-augmented learning (RAL), which retrieves open data, e.g., the VLM's pretraining dataset, to learn models for better serving downstream tasks. RAL has been studied in zero-shot recognition but remains under-explored in FSR. Although applying RAL to FSR may seem straightforward, we observe interesting and novel challenges and opportunities. First, somewhat surprisingly, finetuning a VLM on a large amount of retrieved data underperforms state-of-the-art zero-shot methods. This is due to the imbalanced distribution of retrieved data and its domain gaps with the few-shot examples in the downstream task. Second, more surprisingly, we find that simply finetuning a VLM solely on few-shot examples significantly outperforms previous FSR methods, and finetuning on the mix of retrieved and few-shot data yields even better results. Third, to mitigate the imbalanced distribution and domain gap issues, we propose Stage-Wise retrieval-Augmented fineTuning (SWAT), which involves end-to-end finetuning on mixed data in the first stage and retraining the classifier on the few-shot data in the second stage. Extensive experiments on nine popular benchmarks demonstrate that SWAT significantly outperforms previous methods by >6% accuracy.

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