Patent ID: 11900613
Assignee: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 9:
10. The method according to claim 8, wherein the obtaining a video frame sample, sample annotation information corresponding to the video frame sample, and standard affine transformation information corresponding to the video frame sample comprises:
obtaining a first video frame sample and a second video frame sample, the first video frame sample being a previous video frame of the second video frame sample; and
obtaining sample annotation information respectively corresponding to the first video frame sample and the second video frame sample, and standard affine transformation information corresponding to the first video frame sample;
the inputting the video frame sample into the target segmentation model for training, and determining predicted affine transformation information corresponding to the video frame sample by using the target segmentation model comprises:
inputting the first video frame sample and the second video frame sample into a target segmentation model as a sample pair for training, and processing the first video frame sample by using the target segmentation model to obtain predicted affine transformation information corresponding to the first video frame sample; and
the outputting predicted affine transformation difference information corresponding to the video frame sample and a predicted segmentation result corresponding to a target in the video frame sample by using the target segmentation model comprises:
performing affine transformation on the first video frame sample according to the predicted affine transformation information to obtain a first sample candidate region image, and performing feature extraction on the first sample candidate region image to obtain a first sample feature map;
performing semantic segmentation based on the first sample feature map to obtain a predicted segmentation result corresponding to a target in the first video frame sample; and
revising the predicted affine transformation information according to the first sample feature map to obtain predicted affine transformation difference information corresponding to the first video frame sample.