Semantic Segmentation on VSPW Dataset through Contrastive Loss and Multi-dataset Training Approach

Min Yan, Qianxiong Ning, Qian Wang
6/6/2023
cs.CV

No confident code implementation found for this paper.

Abstract

Video scene parsing incorporates temporal information, which can enhance the consistency and accuracy of predictions compared to image scene parsing. The added temporal dimension enables a more comprehensive understanding of the scene, leading to more reliable results. This paper presents the winning solution of the CVPR2023 workshop for video semantic segmentation, focusing on enhancing Spatial-Temporal correlations with contrastive loss. We also explore the influence of multi-dataset training by utilizing a label-mapping technique. And the final result is aggregating the output of the above two models. Our approach achieves 65.95% mIoU performance on the VSPW dataset, ranked 1st place on the VSPW challenge at CVPR 2023.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Exploiting Temporal State Space Sharing for Video Semantic Segmentation

86% match1 repo132025

Spatio-Temporal Pixel-Level Contrastive Learning-based Source-Free Domain Adaptation for Video Semantic Segmentation

86% match1 repo92023

Deep Common Feature Mining for Efficient Video Semantic Segmentation

86% match1 repo82024

Simultaneously Short- and Long-Term Temporal Modeling for Semi-Supervised Video Semantic Segmentation

86% match2023

SOC: Semantic-Assisted Object Cluster for Referring Video Object Segmentation

85% match1 repo332023

Rethinking Video Segmentation with Masked Video Consistency: Did the Model Learn as Intended?

85% match2024

Cite this paper

@article{yan2023semantic,
  title  = {Semantic Segmentation on VSPW Dataset through Contrastive Loss and Multi-dataset Training Approach},
  author = {Min Yan and Qianxiong Ning and Qian Wang},
  year   = {2023},
  eprint = {2306.03508},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.03508v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2306.03508.svg)](https://codeofpaper.com/paper/2306.03508)

Discussion