Method, computer device, and storage medium, for feature fusion model training and sample retrieval

Abstract

A method for feature fusion model training and sample retrieval includes: inputting training sample into an initial feature fusion model to obtain a training semantic feature and a training global feature, performing classification and recognition based on the training semantic feature to obtain an initial training category, splicing the training semantic feature and the training global feature to obtain a spliced training feature, performing autocorrelation feature calculation based on the spliced training feature to obtain an autocorrelation feature, performing self-attention weight calculation based on the autocorrelation feature to obtain a self-attention weight, and adjusting the spliced training feature through the self-attention weight to obtain a fused training feature; and updating the initial feature fusion model based on the training global feature, the training semantic feature, the fused training feature, the initial training category, and a training sample category label, and performing a loop iteration to obtain a target fusion model.