Segment Anything 3 (SAM3) has established a powerful foundation that robustly detects, segments, and tracks specified targets in videos. However, in its original implementation, its group-level collective memory selection is suboptimal for complex multi-object scenarios, as it employs a synchronized decision across all concurrent targets conditioned on their average performance, often overlooking individual reliability. To this end, we propose SAM3-DMS, a training-free decoupled strategy that utilizes fine-grained memory selection on individual objects. Experiments demonstrate that our approach achieves robust identity preservation and tracking stability. Notably, our advantage becomes more pronounced with increased target density, establishing a solid foundation for simultaneous multi-target video segmentation in the wild.
@article{arxiv.2601.09699,
title = {SAM3-DMS: Decoupled Memory Selection for Multi-target Video Segmentation of SAM3},
author = {Ruiqi Shen and Chang Liu and Henghui Ding},
journal= {arXiv preprint arXiv:2601.09699},
year = {2026}
}