Sa2VA-i: Improving Sa2VA Results with Consistent Training and Inference

3rd Place Report of LSVOS 2025 MeViS Track

Alexey Nekrasov¹ · Ali Athar · Daan de Geus² · Alexander Hermans¹ · Bastian Leibe¹

¹RWTH Aachen University · ²Eindhoven University of Technology

🚀 Overview

Sa2VA-i is an improved version of the popular Sa2VA model for language-guided dense grounding in images and video. While Sa2VA achieves state-of-the-art results on multiple segmentation benchmarks, we identified critical inconsistencies between training and inference procedures that limited its full potential for referring video object segmentation tasks.

Key improvements in Sa2VA-i:

• ✅ Consistent training and inference - eliminates incompatibility between finetuned mask decoder and frozen memory components of SAM2
• ✅ Improved frame sampling - uniform sampling instead of first-frame sampling
• ✅ Better mask propagation - uses original SAM2 weights for propagation while keeping finetuned decoder for initial predictions
• ✅ Significant performance gains - up to +11.6 J&F on MeViS, +1.4 on Ref-YT-VOS, +3.3 on Ref-DAVIS

📊 Performance Highlights

Model	MeViS (J&F)	Ref-YT-VOS (J&F)	Ref-DAVIS17 (J&F)
Sa2VA-1B	47.0	68.0	69.5
Sa2VA-i-1B	52.6	70.3	73.6
Sa2VA-4B	46.4	71.3	73.7
Sa2VA-i-4B	56.6	73.2	78.6
Sa2VA-8B	51.5	72.3	75.9
Sa2VA-i-8B	59.5	73.9	79.1
Sa2VA-26B	52.1	75.1	78.6
Sa2VA-i-26B	63.2	76.5	81.2

Note: Sa2VA-i-1B performs on par with original Sa2VA-26B on MeViS benchmark!

🏆 Competition Results

3rd Place in LSVOS 2025 MeViS Track (RVOS) with 64.1 J&F

🤗 Model Zoo

Sa2VA-i provides improved inference procedures for existing Sa2VA models. Available models:

Model	HuggingFace Repository
Sa2VA-i-1B	kumuji/Sa2VA-i-1B
Sa2VA-i-4B	kumuji/Sa2VA-i-4B
Sa2VA-i-8B	kumuji/Sa2VA-i-8B
Sa2VA-i-26B	kumuji/Sa2VA-i-26B

🎯 Quick Start

For installation and basic usage, please refer to the original Sa2VA repository. Sa2VA-i is a drop-in replacement for inference.

🔧 Key Improvements

1. Consistent Training-Inference

Eliminates incompatibility between finetuned mask decoder and frozen memory components by using the same procedure during both training and inference.

2. Improved Frame Sampling

Replaces first-frame sampling with uniform sampling for better coverage of video content.

3. Original SAM2 Mask Propagation

Uses original SAM2 weights for propagation while keeping finetuned decoder for initial mask predictions.

📚 Citation

If you use Sa2VA-i in your research, please cite:

@article{sa2va2025improved,
  title={Sa2VA-i: Improving Sa2VA Results with Consistent Training and Inference},
  author={Nekrasov, Alexey and Athar, Ali and de Geus, Daan and Hermans, Alexander and Leibe, Bastian},
  journal={arXiv preprint arXiv:2509.19082},
  year={2025}
}

Shout-out to the original Sa2VA paper!

@article{yuan2025sa2va,
  title={Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos},
  author={Yuan, Haobo and Li, Xiangtai and Zhang, Tao and Huang, Zilong and Xu, Shilin and Ji, Shunping and Tong, Yunhai and Qi, Lu and Feng, Jiashi and Yang, Ming-Hsuan},
  journal={arXiv preprint arXiv:2501.04001},
  year={2025}
}