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arXiv Machine Learning By Tom\'a\v{s} \v{C}\'i\v{z}ek, Matej Straka, Illia Volkov, Mat\v{e}j Kripner, Ervin Maci\'c, Jiri Matas, Klara Janouskova, Martin Schmid

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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