Few-sample image sentiment classification method based on meta-learning

Abstract

The invention discloses a few-sample image sentiment classification method based on meta-learning, and the method comprises the steps: firstly constructing a plurality of meta-learning tasks similar to few-sample images with sentiment label information in a target data set on a source data set, obtaining a good classification model initialization parameter through the learning of the meta-learning tasks, so as to enable the classification model to obtain a better classification effect when facing emotion images in a few-sample target data set. According to the method, the need for annotation data can be greatly relieved, the mode based on meta-learning better conforms to the human learning mode (new human learning tasks are learned based on learned tasks), and the neural network model can be more intelligent.

Description

technical field [0001] The invention belongs to the field of neural networks (Neural Networks, NN) in machine learning (Machine Learning), relates to an image emotion classification method based on deep learning, in particular to a few-sample image emotion classification based on meta-learning. Background technique [0002] Some psychological studies have shown that human emotional responses vary with different visual stimuli, and in particular, pictures are very important for attracting people's attention and motivating them to take action. Various previous studies have shown that, for some consumers, pictures can lead to a perception of higher product quality without being able to actually touch the product, making this visual cue all the more important. In e-commerce scenarios, images also have an impact on buyer intent, trust, risk reduction, conversion and click-through rates. In some charitable fundraisers, the combination of positivity and negativity in the "donation...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the problem that the current deep learning method for image emotion is difficult to achieve learning based on limited resources, the purpose of the present invention is to provide an image emotion classification method based on meta-learning, which can realize the emotion classification of few-sample images

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