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

Claim 13:
14. An electronic device comprising a processor and a memory configured to store a computer program, wherein
the processor, when executing the computer program, is caused to perform:
acquiring a target image with a label to be generated;
acquiring a partial image of the target image;
acquiring a plurality of features based on the target image and the partial image, wherein the plurality of features comprises a first feature of the target image and a second feature of the partial image; and
generating a first-type image label of the target image based on the first feature and the second feature,
wherein acquiring the plurality of features based on the target image and the partial image comprises:
inputting the target image into a first feature extracting network;
acquiring the first feature output by the first feature extracting network;
inputting the partial image into a second feature extracting network; and
acquiring the second feature of the partial image output by the second feature extracting network,
wherein the first feature extracting network comprises an intermediate level configured to output an intermediate layer feature and an output level configured to output the first feature, the intermediate layer feature and the first feature having different dimensions, and the plurality of features further comprising the intermediate layer feature; and
the processor, when executing the computer program, is caused to further perform:
generating a second-type image label of the target image based on the intermediate layer feature and the first feature,
wherein generating the second-type image label of the target image based on the intermediate layer feature and the first feature comprises:
acquiring a fused feature based on the intermediate layer feature and the first feature; and
generating the second-type image label based on the fused feature,
wherein acquiring the fused feature based on the intermediate layer feature and the first feature comprises:
inputting the intermediate layer feature and the first feature into a target feature fusing network; and
acquiring the fused feature output by the target feature fusing network,
wherein 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.