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Learning to Focus: Cascaded Feature Matching Network for Few-shot Image Recognition by Chen, Mengting; Wang, Xinggang; Luo, Heng; Geng, Yifeng; Liu, Wenyu is a scholarly article available to read on EtoBox.

What is Learning to Focus: Cascaded Feature Matching Network for Few-shot Image Recognition about?

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 stan

Author

Chen, Mengting; Wang, Xinggang; Luo, Heng; Geng, Yifeng; Liu, Wenyu

Published

2021

Language

EN