Method, apparatus, device, and storage medium for training image semantic segmentation network

An apparatus for training an image semantic segmentation network, comprising:

• at least one memory configured to store program code;
• and
• at least one processor configured to read the program code and operate as instructed by the program code, the program code comprising:
• first training code configured to cause at least one of the at least one processor to train a first image semantic segmentation network based on a training image set, training images in the training image set comprising annotation information;
• masking code configured to cause at least one of the at least one processor to perform mask processing on network weights of the first image semantic segmentation network through a mask network, to obtain a second image semantic segmentation network, the mask network being configured to select network weights in the first image semantic segmentation network that are insensitive to a distribution of image features in the training image set;
• and
• second training code configured to cause at least one of the at least one processor to train the second image semantic segmentation network based on the training image set and a test image set, test images in the test image set comprising no annotation information, and the distribution of image features in the training image set being different from a distribution of image features in the test image set.

A non-transitory computer-readable storage medium storing instructions, the instructions, when executed by one or more processors, causing at least one of the one or more processors to perform:

• training a first image semantic segmentation network based on a training image set, training images in the training image set comprising annotation information;
• performing mask processing on network weights of the first image semantic segmentation network by using a mask network, to obtain a second image semantic segmentation network, the mask network being configured to select network weights in the first image semantic segmentation network that are insensitive to a distribution of image features in the training image set;
• and
• training the second image semantic segmentation network based on the training image set and a test image set, test images in the test image set comprising no annotation information, and the distribution of image features in the training image set being different from a distribution of image features in the test image set.