SAM3RL: Memory Control via Reinforcement Learning for Visual Object Tracking
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SAM3RL introduces a reinforcement‑learning based memory controller for the Segment Anything Model 3 (SAM 3) visual object tracker, deciding which past frames to store in a memory bank to improve tracking performance. This data‑driven approach replaces hand‑crafted memory update rules and achieves state‑of‑the‑art results on 10 of 12 tracking and video object segmentation benchmarks, with no runtime overhead. The same method applied to SAM 2 (SAM2RL) also outperforms hand‑crafted rules on 9 of 12 benchmarks, demonstrating its generality across model generations.
arXiv:2610.01022v1 Announce Type: new Abstract: Memory-attention-based Video Instance Segmentation (VIS) methods have demonstrated strong zero-shot tracking capability, yet their substantial memory r...
arXiv:2608.22064v1 Announce Type: new Abstract: We present our solution for the MOSEv2 track of the 8th Large-scale Video Object Segmentation (LSVOS) Challenge at ECCV 2026. The challenge evaluates r...
arXiv:2602. 14771v5 Announce Type: replace-cross Abstract: The human visual system tracks objects by integrating current observations with previously observed information, adapting to target and scene changes, and reasoning about occlusion at fine granularity.
SAM3Dual is a training‑free inference extension of pretrained SAM 3 that won third place in the MOSEv2 track of the 8th Large‑scale Video Object Segmentation Challenge. It separates temporal memory into short‑term and long‑term branches, fuses their responses deterministically, and modulates them with previous‑frame confidence, all while keeping SAM 3 parameters frozen. The approach achieved an official J&F score of 64.37, demonstrating competitive long‑term VOS performance without task‑specific training.
Segmentation-based multi-object tracking (MOT) with foundation video models such as SAM2 offers strong localization quality, yet remains fragile in crowded, real-world scenes. In detector-prompted SAM...