PVUW 2025 Challenge Report: Advances in Pixel-level Understanding of Complex Videos in the Wild

This report provides a comprehensive overview of the 4th Pixel-level Video Understanding in the Wild (PVUW) Challenge, held in conjunction with CVPR 2025. It summarizes the challenge outcomes, participating methodologies, and future research directions. The challenge features two tracks: MOSE, which focuses on complex scene video object segmentation, and MeViS, which targets motion-guided, language-based video segmentation. Both tracks introduce new, more challenging datasets designed to better reflect real-world scenarios. Through detailed evaluation and analysis, the challenge offers valuable insights into the current state-of-the-art and emerging trends in complex video segmentation. More information can be found on the workshop website: https://pvuw.github.io/.

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Publication Details

Journal
ArXiv.org
Published
2025-04-15
Primary Topic
Multimodal Machine Learning Applications
Type
article
Field-Weighted Citation Impact
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article

PVUW 2025 Challenge Report: Advances in Pixel-level Understanding of Complex Videos in the Wild

ArXiv.org
Multimodal Machine Learning Applications
article

PVUW 2025 Challenge Report: Advances in Pixel-level Understanding of Complex Videos in the Wild

article en

Abstract

This report provides a comprehensive overview of the 4th Pixel-level Video Understanding in the Wild (PVUW) Challenge, held in conjunction with CVPR 2025. It summarizes the challenge outcomes, participating methodologies, and future research directions. The challenge features two tracks: MOSE, which focuses on complex scene video object segmentation, and MeViS, which targets motion-guided, language-based video segmentation. Both tracks introduce new, more challenging datasets designed to better reflect real-world scenarios. Through detailed evaluation and analysis, the challenge offers valuable insights into the current state-of-the-art and emerging trends in complex video segmentation. More information can be found on the workshop website: https://pvuw.github.io/.

ArXiv.org
Fudan University (CN)
Openalex Percentile: Top 49%
Multimodal Machine Learning Applications
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