| title | SAM-MT: Real-Time Interactive Multi-Target Video Segmentation |
|---|---|
| authors | Ruiqi Shen, Chang Liu, Henghui Ding |
| date | 2026-07-09 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.08688 |
| affiliation | FudanCVL |
| upvotes | 7 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Modern Video Object Segmentation (VOS) involves tracking and segmenting user-specified targets. While recent approaches have achieved remarkable performance in single-target scenarios, extending them to multi-target settings typically involves replicating the single-target processing for each individual object, resulting in reduced frame rates (FPS) with unbounded latency as target count increases. Built upon Segment Anything 2 (SAM2), we propose SAM-MT, which addresses this by transforming the model into an interactive framework for real-time Multi-Target video segmentation. SAM-MT uses explicit queries to represent different individual targets, in parallel with a shared representation for global context. It employs decoupled masked attention to keep individual identities distinct from cross-target interference, and sparse memory for stable temporal evolution, along with specialized strategies for occlusion handling and overlap prevention. SAM-MT successfully decouples latency from the number of targets, achieving real-time speed on par with single-target baselines (>36 FPS for 10 targets) while maintaining SAM2's robust video segmentation performance. |
cite
@misc{shen2026sam,
title={SAM-MT: Real-Time Interactive Multi-Target Video Segmentation},
author={Ruiqi Shen and Chang Liu and Henghui Ding},
year={2026},
eprint={2607.08688},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.08688}
}