# Recyclable Semi-supervised Method Based on Multi-model Ensemble for Video Scene Parsing

Source: [Recyclable Semi-supervised Method Based on Multi-model Ensemble for Video Scene Parsing](<https://www.sota2.com/research/paper/68fa7fe41e209131cea9b667>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T06:54:31.635Z

Authors: Biao Wu, Shaoli Liu, Diankai Zhang, Chengjian Zheng, Si Gao, Xiaofeng Zhang, Ning Wang

Published: 2023-06-05T14:04:38.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2306.02894>)

[PDF](<https://arxiv.org/pdf/2306.02894.pdf>)