metadata
license: mit
library_name: DETECTRON2
tags:
- computer-vision
- image-segmentation
- video-instance-segmentation
- deep-learning
- query-enrichment
- pytorch
datasets:
- youtube-vis
- ovis
pipeline_tag: image-segmentation
metrics:
- ap
- ar
base_model:
- deahuang/MinVIS-Model-Zoo
QueenVIS: Rethinking Image-Only Training for Video Instance Segmentation via Query Enrichment
This repository hosts the official model weights and evaluation benchmarks for QueenVIS.
🔗 Quick Links
- Project Page: aut-aisl.github.io/QueenVIS-Project-Page
- GitHub Repository: github.com/ArianKheir/QueenVIS
- Paper (arXiv): 10.48550/arXiv.2607.24598
📖 Model Description
QueenVIS rethinks the paradigm of image-only training for Video Instance Segmentation (VIS). By introducing training-only auxiliary heads for feature prediction and center prediction, QueenVIS embeds dense appearance and spatial priors directly into transformer object queries without requiring video clip supervision.
- Key Feature: Adds zero parameters and zero extra GFLOPs at inference relative to image-only baselines like MinVIS.
- Temporal Association: Employs a training-free confidence-guided query propagation mechanism paired with a non-parametric memory bank to maintain temporal identity across frames.
📊 Datasets & Evaluation Metrics
QueenVIS is evaluated across standard VIS benchmarks:
- YouTube-VIS (2019 / 2021 / 2022 Long Split): Standard and long-sequence video benchmarks.
- OVIS (Occluded Video Instance Segmentation): A challenging benchmark characterized by severe occlusion and complex object interactions.
Performance is measured using standard Video Instance Segmentation metrics:
- AP (Average Precision): Primary metric across IoU thresholds.
- AP₅₀ & AP₇₅: Average Precision at IoU thresholds of 0.50 and 0.75.
- AR₁ & AR₁₀: Average Recall given 1 and 10 detections per video.
📈 Evaluation Results
1. Main Benchmarks (ResNet-50 Backbone)
| Dataset | AP | AP₅₀ | AP₇₅ | AR₁ | AR₁₀ |
|---|---|---|---|---|---|
| YouTube-VIS 2019 | 51.8 | 75.2 | 58.3 | 49.4 | 62.1 |
| YouTube-VIS 2021 | 50.9 | 72.1 | 55.8 | 42.8 | 57.3 |
| YouTube-VIS 2022 (Long) | 33.6 | 56.6 | 34.0 | 31.7 | 39.9 |
| OVIS (Occluded VIS) | 29.8 | 52.7 | 29.0 | 15.4 | 34.6 |
2. Scaling to Vision Transformers (Swin-L Backbone)
| Dataset | AP | AP₅₀ | AP₇₅ | AR₁ | AR₁₀ |
|---|---|---|---|---|---|
| YouTube-VIS 2019 | 63.2 | 85.3 | 68.3 | 55.5 | 68.4 |
| YouTube-VIS 2021 | 59.8 | 81.5 | 65.2 | 48.3 | 64.2 |
| OVIS (Occluded VIS) | 41.0 | 62.2 | 43.0 | 18.4 | 45.0 |
🚀 How to Use
For complete instructions on installation, environment setup, dataset preparation, and inference scripts, visit the official GitHub Repository.
📚 Citation
If you find QueenVIS useful in your research, please cite our work:
@misc{kheirandish2026queenvis,
title={QueenVIS: Rethinking Image-Only Training for Video Instance Segmentation via Query Enrichment},
author={Arian Kheirandish and Fardin Ayar and Ehsan Javanmardi and Manabu Tsukada and Mahdi Javanmardi},
year={2026},
eprint={2607.24598},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={[https://doi.org/10.48550/arXiv.2607.24598](https://doi.org/10.48550/arXiv.2607.24598)}
}