# DVIS-DAQ: Improving Video Segmentation via Dynamic Anchor Queries

arXiv: https://arxiv.org/abs/2404.00086 · HTML: https://mlsanity.com/paper/2404.00086/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 77 references that are in the map.
- First posted on arXiv 29 Mar 2024.

## Builds on (most cited first, 11 of 11)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Vision Transformer Adapter for Dense Predictions](https://mlsanity.com/paper/2205.08534/) (2022)
- [TransTrack: Multiple Object Tracking with Transformer](https://mlsanity.com/paper/2012.15460/) (2020)
- [DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion](https://mlsanity.com/paper/2111.14690/) (2021)
- [Mask2Former for Video Instance Segmentation](https://mlsanity.com/paper/2112.10764/) (2021)
- [SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking](https://mlsanity.com/paper/2111.09621/) (2021)
- [STEP: Segmenting and Tracking Every Pixel](https://mlsanity.com/paper/2102.11859/) (2021)
- [DVIS++: Improved Decoupled Framework for Universal Video Segmentation](https://mlsanity.com/paper/2312.13305/) (2023)
- [Mask Propagation for Efficient Video Semantic Segmentation](https://mlsanity.com/paper/2310.18954/) (2023)
- [GRAtt-VIS: Gated Residual Attention for Auto Rectifying Video Instance Segmentation](https://mlsanity.com/paper/2305.17096/) (2023)
- [NOVIS: A Case for End-to-End Near-Online Video Instance Segmentation](https://mlsanity.com/paper/2308.15266/) (2023)

## Built on by (most cited first, 22 of 22)

- [Sa2VA: Marrying SAM2 with MLLM for Dense Grounded Understanding of Images and Videos](https://mlsanity.com/paper/2501.04001/) (2025)
- [OMG-LLaVA: Bridging Image-level, Object-level, Pixel-level Reasoning and Understanding](https://mlsanity.com/paper/2406.19389/) (2024)
- [VISA: Reasoning Video Object Segmentation via Large Language Models](https://mlsanity.com/paper/2407.11325/) (2024)
- [MOSEv2: A More Challenging Dataset for Video Object Segmentation in Complex Scenes](https://mlsanity.com/paper/2508.05630/) (2025)
- [Are They the Same? Exploring Visual Correspondence Shortcomings of Multimodal LLMs](https://mlsanity.com/paper/2501.04670/) (2025)
- [CoT-RVS: Zero-Shot Chain-of-Thought Reasoning Segmentation for Videos](https://mlsanity.com/paper/2505.18561/) (2025)
- [Dense360: Dense Understanding from Omnidirectional Panoramas](https://mlsanity.com/paper/2506.14471/) (2025)
- [VidEoMT: Your ViT is Secretly Also a Video Segmentation Model](https://mlsanity.com/paper/2602.17807/) (2026)
- [The 1st Solution for 7th LSVOS RVOS Track: SaSaSa2VA](https://mlsanity.com/paper/2509.16972/) (2025)
- [Video Understanding: From Geometry and Semantics to Unified Models](https://mlsanity.com/paper/2603.17840/) (2026)
- [MedMAP: Promoting Incomplete Multi-modal Brain Tumor Segmentation with Alignment](https://mlsanity.com/paper/2408.09465/) (2024)
- [Latest Object Memory Management for Temporally Consistent Video Instance Segmentation](https://mlsanity.com/paper/2507.19754/) (2025)
- [3AM: 3egment Anything with Geometric Consistency in Videos](https://mlsanity.com/paper/2601.08831/) (2026)
- [Beyond Appearance: Geometric Cues for Robust Video Instance Segmentation](https://mlsanity.com/paper/2507.05948/) (2025)
- [PMT: Plain Mask Transformer for Image and Video Segmentation with Frozen Vision Encoders](https://mlsanity.com/paper/2603.25398/) (2026)
- [4th PVUW MeViS 3rd Place Report: Sa2VA](https://mlsanity.com/paper/2504.00476/) (2025)
- [SVAC: Scaling Is All You Need For Referring Video Object Segmentation](https://mlsanity.com/paper/2509.24109/) (2025)
- [SaSaSaSa2VA: 2nd Place of the 5th PVUW MeViS-Text Track](https://mlsanity.com/paper/2603.27241/) (2026)
- [Boosting Text-Driven Video Segmentation via Geometry-Aware Distillation](https://mlsanity.com/paper/2606.24464/) (2026)
- [DynEoMT: Learning Object Dynamicity from Online Segmentation Queries](https://mlsanity.com/paper/2609.14466/) (2026)
- [LVMT: Video Mask Transformer for Long-term Video Segmentation](https://mlsanity.com/paper/2609.34895/) (2026)
- [Learning to Reason with Persistent Object States for Video Instance Segmentation](https://mlsanity.com/paper/2609.35539/) (2026)

## Abstract (arXiv)

Modern video segmentation methods adopt object queries to perform inter-frame association and demonstrate satisfactory performance in tracking continuously appearing objects despite large-scale motion and transient occlusion. However, they all underperform on newly emerging and disappearing objects that are common in the real world because they attempt to model object emergence and disappearance through feature transitions between background and foreground queries that have significant feature gaps. We introduce Dynamic Anchor Queries (DAQ) to shorten the transition gap between the anchor and target queries by dynamically generating anchor queries based on the features of potential candidates. Furthermore, we introduce a query-level object Emergence and Disappearance Simulation (EDS) strategy, which unleashes DAQ's potential without any additional cost. Finally, we combine our proposed DAQ and EDS with DVIS to obtain DVIS-DAQ. Extensive experiments demonstrate that DVIS-DAQ achieves a new state-of-the-art (SOTA) performance on five mainstream video segmentation benchmarks. Code and models are available at urlhttps://github.com/SkyworkAI/DAQ-VS.