# Semi-supervised Video Semantic Segmentation Using Unreliable Pseudo Labels for PVUW2024

Source: [Semi-supervised Video Semantic Segmentation Using Unreliable Pseudo Labels for PVUW2024](<https://www.sota2.com/research/paper/68fa73731e209131cea5c98f>)

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

Data updated: 2025-10-26T02:50:15.439Z

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

Published: 2024-06-02T01:37:26.000Z

## 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. 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.

## 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/2406.00587>)

[PDF](<https://arxiv.org/pdf/2406.00587.pdf>)