Image description generation method based on conditional generative adversarial network

Abstract

An image description generation method based on a conditional generative adversarial network comprises the following steps of 1, constructing a network, wherein a conditional generative adversarial network framework is composed of a generation model and a judgment model, the generation model and the judgment model are similar in structure, but the parameters are independently trained and updated; 2, preprocessing a data set; 3, performing network training, wherein the process comprises 3.1, initializing the parameters of a genearation model and a discrimination model by using the random weights; 3.2, training the generation model; 3.3, training a discrimination model; 3.4, minimizing a loss function by using an RMSprop descent algorithm; and 4, testing the precision, generating the description of the test picture through the operation of the above steps. The image description generation method based on the conditional generative adversarial training is better in robustness and lower in requirement for training data.

Description

technical field [0001] The present invention relates to multimedia big data processing and analysis in the field of computer vision, in particular to a method for generating image descriptions based on conditional generation confrontation, which spans two fields of computer vision and natural language processing. Background technique [0002] With the development of network sharing technology, more and more pictures on the network can be shared and received in real time. How to use a machine to understand the content represented by an image and output it as a coherent and semantically correct sentence has become a key research issue. In recent years, with the rapid development of deep learning methods, thanks to the accurate expression of image content by deep features, significant progress has been made in using machines to automatically generate descriptions. However, these methods have gradient disappearance and image features loss in the network during the training proc...

AI Technical Summary

Problems solved by technology

[0003] In order to overcome the deficiencies of the existing image description generation technology, such as high requirements for training data, monotonous generated descriptions, and unrealistic descriptions, the present invention provides a condition-based adversarial training method with better robustness and lower requirements for training data. Image description generation method

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