Patent ID: 11875554
Assignee: BOE TECHNOLOGY GROUP CO., LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 12:
13. The method according to claim 11, wherein the target image is a painting image, the first-type sample image and the second-type sample image are non-painting images, and the third-type sample image is the painting image;
acquiring the partial image of the target image comprises: acquiring a plurality of partial images of the target image in different sizes, the plurality of partial images comprising a same region of the target image;
prior to acquiring the plurality of features based on the target image and the partial image, the method further comprises: updating the partial image by adjusting a size of the partial image to be a size of the target image;
the first feature extracting network comprises: n intermediate levels and one output level, wherein n>2, an nth intermediate level is connected to the output level, the intermediate level comprises a convolutional layer and a pooling layer, different intermediate levels output intermediate layer features of different dimensions respectively, and the output level comprises a pooling layer and a fully connected layer;
the target feature fusing network comprises: a first-type convolutional layer and n second-type convolutional layers, wherein the first-type convolutional layer is configured to perform convolutional processing on the first feature, an ith second-type convolutional layer is configured to perform convolutional processing on an intermediate layer feature output by an (n+1−i)th intermediate level, and 1≤i≤n; the target feature fusing network further comprises: a first-type up-sampling layer and n−2 second-type up-sampling layers, wherein the first-type up-sampling layer is configured to perform up-sampling on a convolutional feature output by the first-type convolutional layer and a convolutional feature output by a first second-type convolutional layer; a jth second-type up-sampling layer is configured to perform up-sampling on an up-sampling feature output by a previous up-sampling layer and a convolutional feature output by a (j+1)th second-type up-sampling layer, where 1≤j≤n−2; when j=1, the previous up-sampling layer is the first-type up-sampling layer; and when j>1, the previous up-sampling layer is a (j−1)th second-type up-sampling layer; and the fused feature output by the target feature fusing network is a feature added up by an up-sampling feature output by an (n−2)th second-type up-sampling layer and a convolutional feature output by an nth second-type convolutional layer;
the first target label recognizing network is configured to output the first-type image label based on the first feature, the second feature, and information output by a second time recurrent neural network in the second target label recognizing network; and the second target label recognizing network is configured to output the second-type image label based on the fused feature and information output by a first time recurrent neural network in the first target label recognizing network;
the target image is a painting image; the first-type image label is a category label of the target image; and the second-type image label comprises a subject label of the target image and a content label of the target image.