Patent ID: 11972604
Assignee: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. An image feature visualization method, comprising:
acquiring training comparison data, wherein the training comparison data comprises real training data with positive samples and real training data with negative samples;
inputting the real training data with positive samples into a mapping generator that is to be trained to obtain fictitious training data with negative samples output by the mapping generator that is to be trained, wherein the mapping generator comprises a mapper configured to learn a key feature map that distinguishes the real training data with positive samples and the real training data with negative samples, and the fictitious training data with negative samples is generated by the mapping generator based on the real training data with positive samples and the key feature map;
inputting training data with negative samples into a discriminator that is to be trained to obtain a discrimination result output by the discriminator, wherein the training data with negative samples comprises the real training data with negative samples and the fictitious training data with negative samples;
optimizing the mapping generator that is to be trained and the discriminator that is to be trained based on the discrimination result, and returning to execute the step of acquiring the training comparison data and the subsequent steps until training is completed; and
inputting a target image that is to be processed into the trained mapping generator, and extracting features of the target image through the mapper in the trained mapping generator, so as to realize feature visualization of the target image;
wherein the discriminator operates using network layers comprising a convolutional layer, a second-order pooling layer, and a fully-connected layer, and the step of inputting the training data with negative samples into the discriminator that is to be trained to obtain the discrimination result output by the discriminator comprises: inputting a tensorizing expression of the training data with negative samples into the discriminator that is to be trained;
performing feature extraction on the tensorizing expression of the training data with negative samples through the convolution layer to obtain a feature tensor of the training data with negative samples;
performing weighted calculation on the feature tensor of the training data with negative samples through the second-order pooling layer to obtain a weighted feature tensor of the training data with negative samples;
classifying the weighted feature tensor of the training data with negative samples through the fully-connected layer to obtain the discrimination result of the training data with negative samples; and
outputting the discrimination result of the training data with negative samples.