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Report of the 5th PVUW Challenge: Towards More Diverse Modalities in Pixel-Level Understanding

arXiv cs.CV / 4/30/2026

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Key Points

  • The report reviews the goals, datasets, and leading methods from the 2026 Pixel-level Video Understanding in the Wild (PVUW) Challenge held at CVPR 2026.
  • PVUW 2026 evaluates state-of-the-art models under highly unconstrained real-world conditions to benchmark robust pixel-level video scene comprehension.
  • The challenge is organized into three specialized tracks: MOSE for object tracking amid heavy clutter and severe occlusion, MeViS-Text for motion-oriented target localization using linguistic expressions, and the new MeViS-Audio for acoustic-driven object segmentation.
  • It introduces newly released, harder datasets and analyzes top multimodal submissions to map current technical progress and suggest future research directions.
  • The emphasis on multimodal inputs (text and audio alongside video) reflects the community’s push toward more diverse modalities for pixel-level understanding.

Abstract

This report summarizes the objectives, datasets, and top-performing methodologies of the 2026 Pixel-level Video Understanding in the Wild (PVUW) Challenge, hosted at CVPR 2026, which evaluates state-of-the-art models under highly unconstrained conditions. To provide a comprehensive assessment, the 2026 edition features three specialized tracks: the MOSE track for tracking objects within densely cluttered and severely occluded scenarios; the MeViS-Text track for localizing targets via motion-focused linguistic expressions; and the newly inaugurated MeViS-Audio track, which pioneers acoustic-driven object segmentation. By introducing previously unreleased challenging data and analyzing the cutting-edge, multimodal solutions submitted by participants, this report highlights the community's latest technical advancements and charts promising future directions for robust video scene comprehension.

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