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

arXiv: https://arxiv.org/abs/2604.26031 · HTML: https://mlsanity.com/paper/2604.26031/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 32 references that are in the map.
- First posted on arXiv 28 Apr 2026.

## Builds on (most cited first, 28 of 28)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [DINOv3](https://mlsanity.com/paper/2508.10104/) (2025)
- [Reproducible scaling laws for contrastive language-image learning](https://mlsanity.com/paper/2212.07143/) (2022)
- [SAM 3: Segment Anything with Concepts](https://mlsanity.com/paper/2511.16719/) (2025)
- [MOSE: A New Dataset for Video Object Segmentation in Complex Scenes](https://mlsanity.com/paper/2302.01872/) (2023)
- [MeViS: A Large-scale Benchmark for Video Segmentation with Motion Expressions](https://mlsanity.com/paper/2308.08544/) (2023)
- [Towards Open Vocabulary Learning: A Survey](https://mlsanity.com/paper/2306.15880/) (2023)
- [Sa2VA: Marrying SAM2 with MLLM for Dense Grounded Understanding of Images and Videos](https://mlsanity.com/paper/2501.04001/) (2025)
- [VISA: Reasoning Video Object Segmentation via Large Language Models](https://mlsanity.com/paper/2407.11325/) (2024)
- [Transformer-Based Visual Segmentation: A Survey](https://mlsanity.com/paper/2304.09854/) (2023)
- [Qwen3-ASR Technical Report](https://mlsanity.com/paper/2601.21337/) (2026)
- [Boundary-Aware Feature Propagation for Scene Segmentation](https://mlsanity.com/paper/1909.00179/) (2019)
- [T-Rex2: Towards Generic Object Detection via Text-Visual Prompt Synergy](https://mlsanity.com/paper/2403.14610/) (2024)
- [MOSEv2: A More Challenging Dataset for Video Object Segmentation in Complex Scenes](https://mlsanity.com/paper/2508.05630/) (2025)
- [Semantic Correlation Promoted Shape-Variant Context for Segmentation](https://mlsanity.com/paper/1909.02651/) (2019)
- [A Survey of Multimodal-Guided Image Editing with Text-to-Image Diffusion Models](https://mlsanity.com/paper/2406.14555/) (2024)
- [Towards Robust Referring Video Object Segmentation with Cyclic Relational Consensus](https://mlsanity.com/paper/2207.01203/) (2022)
- [MeViS: A Multi-Modal Dataset for Referring Motion Expression Video Segmentation](https://mlsanity.com/paper/2512.10945/) (2025)
- [Exploiting Temporal State Space Sharing for Video Semantic Segmentation](https://mlsanity.com/paper/2503.20824/) (2025)
- [PVUW 2024 Challenge on Complex Video Understanding: Methods and Results](https://mlsanity.com/paper/2406.17005/) (2024)
- [LSVOS Challenge Report: Large-scale Complex and Long Video Object Segmentation](https://mlsanity.com/paper/2409.05847/) (2024)
- [The 1st Solution for 7th LSVOS RVOS Track: SaSaSa2VA](https://mlsanity.com/paper/2509.16972/) (2025)
- [PVUW 2025 Challenge Report: Advances in Pixel-level Understanding of Complex Videos in the Wild](https://mlsanity.com/paper/2504.11326/) (2025)
- [Just a Few Glances: Open-Set Visual Perception with Image Prompt Paradigm](https://mlsanity.com/paper/2412.10719/) (2024)
- [LSVOS 2025 Challenge Report: Recent Advances in Complex Video Object Segmentation](https://mlsanity.com/paper/2510.11063/) (2025)
- [VIRST: Video-Instructed Reasoning Assistant for SpatioTemporal Segmentation](https://mlsanity.com/paper/2603.27060/) (2026)

## Built on by (most cited first, 1 of 1)

- [MLLM-Assisted Audio VOS: A 3rd Place Report for the MeViS-Audio Track, 8th LSVOS Challenge](https://mlsanity.com/paper/2608.23234/) (2026)

## Abstract (arXiv)

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.