  Pixel-level Scene Understanding is one of the fundamental problems in
computer vision, which aims at recognizing object classes, masks and semantics
of each pixel in the given image. Compared with image scene parsing, video
scene parsing introduces temporal information, which can effectively improve
the consistency and accuracy of prediction,because the real-world is actually
video-based rather than a static state. In this paper, we adopt semi-supervised
video semantic segmentation method based on unreliable pseudo labels. Then, We
ensemble the teacher network model with the student network model to generate
pseudo labels and retrain the student network. Our method achieves the mIoU
scores of 63.71% and 67.83% on development test and final test respectively.
Finally, we obtain the 1st place in the Video Scene Parsing in the Wild
Challenge at CVPR 2024.
