Learning a Few-shot Embedding Model with Contrastive Learning

Few-shot learning (FSL) aims to recognize target classes by adapting the prior knowledge learned from source classes. Such knowledge usually resides in a deep embedding model for a general matching purpose of the support and query image pairs. The objective of this paper is to repurpose the contrastive learning for such matching to learn a few-shot embedding model. We make the following contributions: (i) We investigate the contrastive learning with Noise Contrastive Estimation (NCE) in a supervised manner for training a fewshot embedding model; (ii) We propose a novel contrastive training scheme dubbed infoPatch, exploiting the patch-wise relationship to substantially improve the popular infoNCE; (iii) We show that the embedding learned by the proposed infoPatch is more effective; (iv) Our model is thoroughly evaluated on few-shot recognition task; and demonstrates state-ofthe-art results on miniImageNet and appealing performance on tieredImageNet, Fewshot-CIFAR100 (FC-100).

Authors

• Chen Liu
• Yanwei Fu Fudan University Shanghai Innovation Institute
• Chengming Xu
• Siqian Yang Tencent
• Jilin Li
• Chengjie Wang Tencent Youtu Lab Shanghai Jiao Tong University
• Li Zhang

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