Multi-Level Correlation Network For Few-Shot Image Classification

• URL: http://arxiv.org/abs/2412.03159v1
• Date: Wed, 04 Dec 2024 09:36:24 GMT
• Title: Multi-Level Correlation Network For Few-Shot Image Classification
• Authors: Yunkai Dang, Min Zhang, Zhengyu Chen, Xinliang Zhang, Zheng Wang, Meijun Sun, Donglin Wang,
• Abstract summary: Few-shot image classification aims to recognize novel classes given few labeled images from base classes. We propose a multi-level correlation network (MLCN) for FSIC to tackle this problem by effectively capturing local information.
• Score: 36.44416763952161
• License:
• Abstract: Few-shot image classification(FSIC) aims to recognize novel classes given few labeled images from base classes. Recent works have achieved promising classification performance, especially for metric-learning methods, where a measure at only image feature level is usually used. In this paper, we argue that measure at such a level may not be effective enough to generalize from base to novel classes when using only a few images. Instead, a multi-level descriptor of an image is taken for consideration in this paper. We propose a multi-level correlation network (MLCN) for FSIC to tackle this problem by effectively capturing local information. Concretely, we present the self-correlation module and cross-correlation module to learn the semantic correspondence relation of local information based on learned representations. Moreover, we propose a pattern-correlation module to capture the pattern of fine-grained images and find relevant structural patterns between base classes and novel classes. Extensive experiments and analysis show the effectiveness of our proposed method on four widely-used FSIC benchmarks. The code for our approach is available at: https://github.com/Yunkai696/MLCN.

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