Objective

2

Deep learning has made remarkable achievements in many fields such as image recognition

object detection

and speech recognition. However

most of the extraordinary achievements of these models depend on extraordinary data size. Existing deep-learning models often need large-scale training data. Building large-scale training data sets not only necessitates a large amount of manpower and material resources but are also not feasible in scenarios such as obtaining a large number of rare image class data samples. Inspired by the fact that human children can learn how to distinguish an object through a small number of samples

few-shot image classification aims to identify target categories with only a few labeled samples. Image recognition based on few-shot learning solves the problem in which a deep learning model needs large-scale training data. At present

the mainstream methods of few-shot image recognition are based on meta learning

which mainly includes three methods: meta learning based on metric

meta learning based on optimization

and meta learning based on model. The method of meta learning is divided into two stages: training and testing. However

most of the metric-based meta-learning methods do not use few shots of the target class in the training stage

which leads to a lack of good generalization ability of these models. These metric-based meta-learning models often show high accuracy in the training stage

but the recognition effect for few-shot image categories in the test stage is poor. The deep feature representation learned by the models cannot be effectively generalized to the target class. To improve the generalization ability of the few-shot learning image recognition method

this study proposes a few-shot learning method based on class semantic similarity supervision.

Method

2

The method proposed in this paper mainly includes two parts: the first step is to obtain the class similarity matrix between the image dataset classes

and the second step is to use the class similarity matrix as additional supervision information to train the few-shot image recognition model. The details are as follows: a common crawl database containing one billion level webpage data is used to train an unsupervised word-vector learning algorithm GloVe model (global vectors for word representation)

which generates 300 dimensional vectors for every word. For classes whose names contain more than one word

we match all the words in the training GloVe model and find their word-embedding vectors. By averaging these word-embedding vectors

we obtain the word embedding vector of the class name. Then

the cosine distance between the word-embedding vectors of classes is used to represent the semantic similarity between classes. In addition to the negative logarithm loss caused by the category labels of the original prototypical networks

this study introduces the semantic similarity measure between categories as the extra supervision information in the training stage of the model to establish the implicit relationship between the source class and few-shot target class. This condition enables the model to have better generalization ability. Furthermore

the loss of class semantic similarity can constrain the features of samples within and between classes learned by the model so that the sample features within each class are more similar

and the distribution of sample features between different classes is more consistent with the semantic similarity between categories. By introducing the loss of class semantic similarity to supervise the training process of the model

our proposed model can implicitly learn the relationship between different classes and obtain a feature representation with more constraint and generalization abilities of class sample features.

Result

2

This study compared the proposed model with several state-of-the-art few-shot image classification models

including prototypical

matching

and relation networks and other classic methods. In this study

a large number of experiments are conducted on miniImageNet and tieredImageNet. The results show that the proposed method is effective and competitive with the current advanced methods. To ensure fair comparison with the advanced methods

the classical paradigm of meta learning is used to train and test the model

and many experiments are conducted on the widely used 5-way 1-shot and 5-way 5-shot settings. The experimental results show that on the 5-way 1-shot and 5-way 5-shot settings of the miniImageNet dataset

the classification accuracy of the proposed method is improved by 1.9% and 0.32%

respectively

compared with the classical few-shot image recognition meta-learning method prototypical networks. In the tieredImageNet dataset on the 5-way 1-shot setting

the classification accuracy rate is improved by 0.33% compared with that in the prototypical networks. On the 5-way 5-shot setting of the tieredImageNet dataset

the proposed model achieves a competitive result compared with the prototypical networks. At the same time

several ablation experiments are conducted to verify the effectiveness of the key modules of the proposed method

and the influence of prior information of class semantic similarity on the experimental results is analyzed from multiple perspectives.

Conclusion

2

This study proposed a few-shot image recognition model based on class semantic similarity supervision

which improves the generalization ability and class-feature constraint ability of the few-shot image recognition model. Experimental results show that the proposed method improves the accuracy of few-shot image recognition.