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

01/13/2021
by   Mengting Chen, et al.
0

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, miniImageNet 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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 11

page 12

02/10/2018

Deep Meta-Learning: Learning to Learn in the Concept Space

Few-shot learning remains challenging for meta-learning that learns a le...
04/22/2022

Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition

One-shot fine-grained visual recognition often suffers from the problem ...
09/25/2019

A Theoretical Analysis of the Number of Shots in Few-Shot Learning

Few-shot classification is the task of predicting the category of an exa...
12/10/2019

A Two-Stage Approach to Few-Shot Learning for Image Recognition

This paper proposes a multi-layer neural network structure for few-shot ...
05/11/2018

Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examples

Humans are capable of learning a new fine-grained concept with very litt...
01/06/2020

Few-shot Learning with Multi-scale Self-supervision

Learning concepts from the limited number of datapoints is a challenging...
04/23/2018

Memory Matching Networks for One-Shot Image Recognition

In this paper, we introduce the new ideas of augmenting Convolutional Ne...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.