Multi-task attribute image recognition method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a multi-task attribute image recognition method and device, electronic equipment and a storage medium. The method comprises: obtaining a to-be-identified image needing to be input into the target identification network, wherein the target recognition network is obtained by training a multi-task attribute recognition network through sample data obtained through an active learning method, and the target recognition network comprises a shared network used for extracting public image features and a plurality of task networks used for extracting task imagefeatures; inputting the to-be-identified image into the shared network for image feature extraction to obtain public image features of the to-be-identified image; inputting the public image features into the task network for task feature extraction to obtain task image features of the to-be-identified image; and performing task result classification based on the task image features to obtain an attribute identification result. Time and calculation expenditure are saved, the model operation speed is increased, and the cost-to-effect ratio of multi-attribute identification is reduced.

Description

technical field [0001] The present invention relates to the field of artificial intelligence, in particular to an image recognition method, device, electronic equipment and storage medium with multi-task attributes. Background technique [0002] With the in-depth research on artificial intelligence, image recognition technology continues to land. In the image recognition technology, the user establishes an initial image recognition network, and then continuously trains the initial image recognition network through the marked data set, so that the classification result of the initial image recognition network for the image is getting closer and closer to the marked data result. Thus, the corresponding image recognition network is obtained. In the image recognition network, usually a recognition task is used as a model for image recognition. In this way, when multi-attribute recognition is required, it is necessary to design a network for a separate attribute and prepare a se...

AI Technical Summary

Problems solved by technology

In the image recognition network, usually a recognition task is used as a model for image recognition. In this way, when multi-attribute recognition is required, it is necessary to design a network for a separate attribute and prepare a separate training data, resulting in a large amount of model data and parameters. Long calculation time, high computational overhead and troublesome training process

Therefore, in the existing image recognition technology, since multi-attribute recognition is performed through multiple recognition networks, multi-attribute recognition needs to be trained for multiple recognition networks, and then multiple training data sets, which are costly and ineffective, so there is cost-effective problem

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