Distribution Consistency Based Covariance Metric Networks for Few-Shot Learning

By Wenbin Li, Jinglin Xu, Jing Huo, Yang Gao, Jiebo Luo. 2019

249 citations recorded by the source corpus.

Few-shot learning aims to recognize new concepts from very few examples. However, most of the existing few-shot learning methods mainly concentrate on the first-order statistic of concept representation or a fixed metric on the relation between a sample and a concept. In this work, we propose a novel end-to-end deep architecture, named Covariance Metric Networks (CovaMNet). The CovaMNet is designed to exploit both the covariance representation and covariance metric based on the distribution consistency for the few-shot classification tasks. Specifically, we construct an embedded local covariance representation to extract the second-order statistic information of each concept and describe the underlying distribution of this concept. Upon the covariance representation, we further define a new deep covariance metric to measure the consistency of distributions between query samples and new concepts. Furthermore, we employ the episodic training mechanism to train the entire network in an end-to-end manner from scratch. Extensive experiments in two tasks, generic few-shot image classification and fine-grained fewshot image classification, demonstrate the superiority of the proposed CovaMNet. The source code can be available from https://github.com/WenbinLee/CovaMNet.git.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

On Generalizing Beyond Domains in Cross-Domain Continual Learning (2022, background): “To name a few, the Contrastive Adaptation Network (CAN) for unsupervised domain adaptation [17], the Covariance Metric Networks (CovaMNet) for few-shot learning [22], the Model-Agnostic learning of Semantic Features (MASF) for standard domain generalization [6] and”
Cross-Domain Similarity Learning for Face Recognition in Unseen Domains (2021, methodology): “Such similarity metrics are encoded by means of covariance matrices, borrowing the idea from [31].”
Revisiting Local Descriptor Based Image-To-Class Measure for Few-Shot Learning (2019, methodology): “The metric-learning based methods mainly depend on learning an informative similarity metric, as demonstrated by the representative work [9, 22, 20, 17, 4, 25, 11].”