  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. Since the real-world is actually video-based
rather than a static state, learning to perform video semantic segmentation is
more reasonable and practical for realistic applications. In this paper, we
adopt Mask2Former as architecture and ViT-Adapter as backbone. Then, we propose
a recyclable semi-supervised training method based on multi-model ensemble. Our
method achieves the mIoU scores of 62.97% and 65.83% on Development test and
final test respectively. Finally, we obtain the 2nd place in the Video Scene
Parsing in the Wild Challenge at CVPR 2023.
