Model training method and device for image classification and storage medium

A model training and image technology, applied in the field of computer vision, can solve problems such as poor results, achieve the effect of improving discrimination and alleviating domain bias problems

Pending Publication Date: 2022-03-11
云鹏智汇(深圳)科技有限公司 +1
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Problems solved by technology

[0004] The embodiment of the present application provides a model training method, device and storage medium for image classification, to solve the problem that the model trained by the existing model training method for image classification has poor effect in image classification technical problem

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  • Model training method and device for image classification and storage medium
  • Model training method and device for image classification and storage medium
  • Model training method and device for image classification and storage medium

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Embodiment Construction

[0021] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in conjunction with specific embodiments of the present application and corresponding drawings. Apparently, the described embodiments are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

[0022] With the widespread use of deep learning model frameworks, supervised learning has achieved many outstanding results in the field of image recognition. This is due to the increasing number of labeled data sets available for training, and the deep learning model can continuously improve the recognition accuracy of the model through sufficient training. However, the e...

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Abstract

The invention discloses a model training method and device for image classification and a storage medium, and is used for solving the technical problem that a model obtained by an existing model training method cannot achieve a better image classification effect. The method comprises the steps of obtaining a visual feature vector of a sample picture; extracting a shallow semantic feature and a deep semantic feature in the visual feature vector based on a preset algorithm, and integrating the shallow semantic feature and the deep semantic feature to obtain a joint semantic feature; performing semantic space alignment on the joint semantic features to obtain a semantic alignment loss function; reconstructing the visual features, and determining an auto-encoder loss function according to the reconstructed visual features; and determining a target function training neural network model based on the semantic alignment loss function, the auto-encoder loss function and a preset parameter regular term. According to the method, the discrimination of the semantic embedding space is improved, and the domain bias problem of the zero sample learning model is relieved.

Description

technical field [0001] The present application relates to the technical field of computer vision, and in particular to a model training method, device and storage medium for image classification. Background technique [0002] With the widespread use of deep learning model frameworks, supervised learning has achieved many outstanding results in the field of image recognition. The deep learning model can continuously improve the recognition accuracy of the model through sufficient training. However, the existing supervised image recognition methods can only recognize the categories that have appeared in the data set. In most practical application scenarios, it takes a lot of time to mark a large amount of data. To solve this problem, the researchers proposed Zero-shot learning is used to identify categories that do not appear in the training set, and zero-shot learning aims to classify unseen categories through the knowledge learned in visible categories. [0003] Currently,...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V10/774G06V10/764G06V10/77G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/2135G06F18/214G06F18/241
Inventor 曹伟朋吴宇豪庄浩蔡恒刘鑫
Owner 云鹏智汇(深圳)科技有限公司
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