The 5th PVUW challenge will be held in conjunction with CVPR 2026 in Denver CO. 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 segmentation is more reasonable and practical for realistic applications. To advance the segmentation task from images to videos, we will present new datasets and competitions in this workshop, aiming at performing the challenging yet practical Pixel-level Video Understanding in the Wild (PVUW). This workshop will cover but not limit to the following topics:

• Semantic/panoptic segmentation for images/videos
• Referring image/video comprehension/segmentation
• Video object/instance segmentation
• Video understanding in complex environments
• Language-guided video understanding
• Audio-guided video segmentation
• Efficient computation for video scene parsing
• Semi-supervised recognition in videos
• New metrics to evaluate the quality of video scene parsing results
• Real-world video applications, e.g., autonomous driving, indoor robotics, visual navigation, etc.

[Update] PVUW 2026 will be using [OpenReview] to manage submissions. We are looking forward to your work and engaging discussions at the workshop!

We invite authors to submit unpublished papers (8-page CVPR format) to our workshop, to be presented at a poster session upon acceptance. All submissions will go through a double-blind review process. All contributions must be submitted (along with supplementary materials, if any) through the paper submission portal.

Accepted papers will be published in the official CVPR Workshops proceedings and the Computer Vision Foundation (CVF) Open Access archive.