Abstract
Few-shot learning is increasingly popular in image classification. The key is to learn the significant features from source classes to match the support and query pairs. In this paper, we redesign the contrastive learning scheme in a few-shot manner with selected proposal boxes generated by Navigator network. The main work of this paper includes: (i) We analyze the limitation of hard sample generating proposed by current few-shot learning methods with contrastive learning and find additional noise introduced in contrastive loss construction. (ii) We propose a novel embedding model with contrastive learning named infoPB which improves hard samples with proposal boxes to improve Noise Contrastive Estimation. (iii) We demonstrate infoPB is effective in few-shot image classification and benefited from Navigator network through the ablation study. (iv) The performance of our method is evaluated thoroughly on typical few-shot image classification tasks. It verifies a new state-of-the-art performance compared with outstanding competitors with their best results on miniImageNet in 5-way, 5-shot, and tieredImageNet in 5-way, 1-shot/5-way, 5-shot.
This work was supported by Support Scheme of Guangzhou for Leading Talents in Innovation and Entrepreneurship (No: 2020010).
F. Gong and Y. Xie—Equal contribution.
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Gong, F. et al. (2023). A Proposal-Improved Few-Shot Embedding Model with Contrastive Learning. In: Dang-Nguyen, DT., et al. MultiMedia Modeling. MMM 2023. Lecture Notes in Computer Science, vol 13834. Springer, Cham. https://doi.org/10.1007/978-3-031-27818-1_17
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