Enhancing Prototypical Few-Shot Learning by Leveraging the Local-Level Strategy

• URL: http://arxiv.org/abs/2111.04331v1
• Date: Mon, 8 Nov 2021 08:45:15 GMT
• Title: Enhancing Prototypical Few-Shot Learning by Leveraging the Local-Level Strategy
• Authors: Junying Huang, Fan Chen, Keze Wang, Liang Lin, and Dongyu Zhang
• Abstract summary: We find that the existing works often build their few-shot model based on the image-level feature by mixing all local-level features. We present (a) a local-agnostic training strategy to avoid the discriminative location bias between the base and novel categories, and (b) a novel local-level similarity measure to capture the accurate comparison between local-level features.
• Score: 75.63022284445945
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aiming at recognizing the samples from novel categories with few reference samples, few-shot learning (FSL) is a challenging problem. We found that the existing works often build their few-shot model based on the image-level feature by mixing all local-level features, which leads to the discriminative location bias and information loss in local details. To tackle the problem, this paper returns the perspective to the local-level feature and proposes a series of local-level strategies. Specifically, we present (a) a local-agnostic training strategy to avoid the discriminative location bias between the base and novel categories, (b) a novel local-level similarity measure to capture the accurate comparison between local-level features, and (c) a local-level knowledge transfer that can synthesize different knowledge transfers from the base category according to different location features. Extensive experiments justify that our proposed local-level strategies can significantly boost the performance and achieve 2.8%-7.2% improvements over the baseline across different benchmark datasets, which also achieves state-of-the-art accuracy.

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