Dual spatial constraints-based few-shot image classification

Abstract

As one of the development directions of artificial intelligence in the future, few-shot learning has attracted more and more attention in recent years. How to make full use of the information of a small amount of samples is one of the main difficulties in the field of few-shot learning. Most of the research work utilizes the meta-learning mechanism to alleviate the negative impact of insufficient samples on model performance. However, the training between subtasks also makes it difficult for meta-learning models to obtain general feature representations between samples. Therefore, researchers are turning their research perspective to supervised learning, and they have drawn a conclusion that embedding models with good performance are simpler and more effective than complex meta-learning models. Recent research work has also proved the importance of feature representation. Based on the above view points and analysis, we propose a few-shot image classification method, which strengthens the difference of samples from different categories and the similarity of samples from the same category and realizes dual constraints in high-dimensional feature space and low-dimensional feature space. Experimental results on four public datasets demonstrate that the proposed method effectively improves the accuracy of image classification with few-shot learning.