Learning to Focus: Cascaded Feature Matching Network for Few-shot Image Recognition

TL;DR

The paper introduces a Cascaded Feature Matching Network (CFMN) that improves few-shot image recognition by focusing on highly correlated features and incorporating multi-scale information, leading to better generalization and performance.

Contribution

It proposes a novel feature matching block for deep metric learning in few-shot recognition, enhancing adaptability and multi-scale feature integration.

Findings

Effective on miniImageNet and Omniglot datasets.

Outperforms existing methods in few-shot learning tasks.

First to study multi-label few-shot learning on COCO.

Abstract

Deep networks can learn to accurately recognize objects of a category by training on a large number of annotated images. However, a meta-learning challenge known as a low-shot image recognition task comes when only a few images with annotations are available for learning a recognition model for one category. The objects in testing/query and training/support images are likely to be different in size, location, style, and so on. Our method, called Cascaded Feature Matching Network (CFMN), is proposed to solve this problem. We train the meta-learner to learn a more fine-grained and adaptive deep distance metric by focusing more on the features that have high correlations between compared images by the feature matching block which can align associated features together and naturally ignore those non-discriminative features. By applying the proposed feature matching block in different layers of the few-shot recognition network, multi-scale information among the compared images can be incorporated into the final cascaded matching feature, which boosts the recognition performance further and generalizes better by learning on relationships. The experiments for few-shot learning on two standard datasets, \emph{mini}ImageNet and Omniglot, have confirmed the effectiveness of our method. Besides, the multi-label few-shot task is first studied on a new data split of COCO which further shows the superiority of the proposed feature matching network when performing few-shot learning in complex images. The code will be made publicly available.