Patent ID: 11887354
Assignee: INSTITUTE OF AUTOMATION, CHINESE ACADEMY OF SCIENCES
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 5:
6. A weakly supervised image semantic segmentation system based on an intra-class discriminator, using the weakly supervised image semantic segmentation method based on the intra-class discriminator according to claim 1, comprising an input module, a feature extraction module, an image semantic segmentation module, and an output module, wherein the input module is configured to obtain a to-be-processed image or obtain a training image set and corresponding image-level class labels;
the feature extraction module is configured to extract a feature image of the to-be-processed image or extract a feature image set corresponding to the training image set;
the image semantic segmentation module is configured to obtain an image semantic segmentation result corresponding to the to-be-processed image based on the feature image corresponding to the to- be-processed image; and
the output module is configured to output the image semantic segmentation result corresponding to the to-be-processed image, wherein
the image semantic segmentation module is obtained through training based on the training image set and corresponding accurate pixel-level class labels; and the accurate pixel-level class labels are obtained through a first intra-class discriminator, a second intra-class discriminator and a class label generation module based on the training image set and the corresponding image-level class labels;
the first intra-class discriminator comprises a first loss calculation module and a first circulation module; the first loss calculation module calculates a first loss value based on a training feature image set, the corresponding image-level class labels, and a first loss function; and the first circulation module is configured to update a parameter of the first intra-class discriminator and perform a first cyclic training until a set first quantity of times of training is reached, wherein a trained first intra-class discriminator and preliminary pixel-level foreground and background labels corresponding to all classes of each image in the training image set are obtained;
the second intra-class discriminator comprises a second loss calculation module and a second circulation module; the second loss calculation module calculates a second loss value based on the training feature image set, the preliminary pixel-level foreground and background labels corresponding to all the classes of each image in the training image set, and a second loss function; and the second circulation module is configured to update a parameter of the second intra-class discriminator and perform a second cyclic training until a set second quantity of times of training is reached, wherein a trained second intra-class discriminator and accurate pixel-level foreground and background labels corresponding to all the classes of each image in the training image set are obtained; and
the class label generation module is configured to generate the accurate pixel-level class labels based on the accurate pixel-level foreground and background labels corresponding to all the classes of each image in the training image set and the corresponding image-level class labels.