Image segmentation method and apparatus, model training method and apparatus, device, and storage medium

An image segmentation apparatus, comprising at least one memory and at least one processor, the at least one memory storing a computer program, the computer program, when executed by the at least one processor, causing the at least one processor to:

• obtain a current frame and historical affine transformation information of a previous video frame in a video frame sequence, wherein the historical affine transformation information of the previous video frame includes parameters obtained from an image segmentation processing of the previous video frame and revised for affine transformation of the current frame;
• perform the affine transformation on the current frame according to the historical affine transformation information to obtain a candidate region image corresponding to the current frame;
• perform feature extraction on the candidate region image to obtain a feature map corresponding to the candidate region image;
• perform semantic segmentation based on the feature map to obtain a segmentation result corresponding to a target in the current frame;
• and
• revise the historical affine transformation information according to the feature map to obtain updated affine transformation information, and use the updated affine transformation information as historical affine transformation information corresponding to a subsequent video frame in the video frame sequence.

A non-transitory computer-readable storage medium, storing a computer program, the computer program, when executed by at least one processor, causing the at least one processor to perform the operations of the method comprising:

• obtaining a current frame and historical affine transformation information of a previous video frame in a video frame sequence, wherein the historical affine transformation information of the previous video frame includes parameters obtained from an image segmentation processing of the previous video frame and revised for affine transformation of the current frame;
• performing the affine transformation on the current frame according to the historical affine transformation information to obtain a candidate region image corresponding to the current frame;
• performing feature extraction on the candidate region image to obtain a feature map corresponding to the candidate region image;
• performing semantic segmentation based on the feature map to obtain a segmentation result corresponding to a target in the current frame;
• and
• revising the historical affine transformation information according to the feature map to obtain updated affine transformation information, and using the updated affine transformation information as historical affine transformation information corresponding to a subsequent video frame in the video frame sequence.