Multi-Level Correlation Network For Few-Shot Image Classification
This work addresses the challenge of generalizing from base to novel classes with limited labeled images in image classification, representing an incremental improvement over existing metric-learning methods.
The paper tackles the problem of few-shot image classification by proposing a multi-level correlation network (MLCN) that captures local information through self-correlation, cross-correlation, and pattern-correlation modules, achieving improved performance on four widely-used benchmarks.
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.