作者
Baoquan Zhang, Xutao Li, Yunming Ye, Shanshan Feng
发表日期
2023/5/22
期刊
IEEE Transactions on Pattern Analysis and Machine Intelligence
卷号
45
期号
10
页码范围
12250-12268
出版商
IEEE
简介
Few-shot learning (FSL) aims to recognize novel classes with few examples. Pre-training based methods effectively tackle the problem by pre-training a feature extractor and then fine-tuning it through the nearest centroid based meta-learning. However, results show that the fine-tuning step makes marginal improvements. In this paper, 1) we figure out the reason, i.e., in the pre-trained feature space, the base classes already form compact clusters while novel classes spread as groups with large variances, which implies that fine-tuning feature extractor is less meaningful; 2) instead of fine-tuning feature extractor, we focus on estimating more representative prototypes. Consequently, we propose a novel prototype completion based meta-learning framework. This framework first introduces primitive knowledge (i.e., class-level part or attribute annotations) and extracts representative features for seen attributes as priors …
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B Zhang, X Li, Y Ye, S Feng - IEEE Transactions on Pattern Analysis and Machine …, 2023