Collaboration of Pre-trained Models Makes Better Few-shot Learner

• URL: http://arxiv.org/abs/2209.12255v1
• Date: Sun, 25 Sep 2022 16:23:12 GMT
• Title: Collaboration of Pre-trained Models Makes Better Few-shot Learner
• Authors: Renrui Zhang, Hanqiu Deng, Bohao Li, Wei Zhang, Hao Dong, Hongsheng Li, Peng Gao, Yu Qiao
• Abstract summary: Few-shot classification requires deep neural networks to learn generalized representations only from limited training images. Recently, CLIP-based methods have shown promising few-shot performance benefited from the contrastive language-image pre-training. We propose CoMo, a Collaboration of pre-trained Models that incorporates diverse prior knowledge from various pre-training paradigms for better few-shot learning.
• Score: 49.89134194181042
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Few-shot classification requires deep neural networks to learn generalized representations only from limited training images, which is challenging but significant in low-data regimes. Recently, CLIP-based methods have shown promising few-shot performance benefited from the contrastive language-image pre-training. Based on this point, we question if the large-scale pre-training can alleviate the few-shot data deficiency and also assist the representation learning by the pre-learned knowledge. In this paper, we propose CoMo, a Collaboration of pre-trained Models that incorporates diverse prior knowledge from various pre-training paradigms for better few-shot learning. Our CoMo includes: CLIP's language-contrastive knowledge, DINO's vision-contrastive knowledge, and DALL-E's language-generative knowledge. Specifically, CoMo works in two aspects: few-shot data expansion and diverse knowledge ensemble. For one, we generate synthetic images via zero-shot DALL-E to enrich the few-shot training data without any manpower. For the other, we introduce a learnable Multi-Knowledge Adapter (MK-Adapter) to adaptively blend the predictions from CLIP and DINO. By such collaboration, CoMo can fully unleash the potential of different pre-training methods and unify them to perform state-of-the-art for few-shot classification. We conduct extensive experiments on 11 datasets to demonstrate the superiority and generalization ability of our approach.

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