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A Distractor-Aware Memory for Visual Object Tracking with SAM2

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arxiv 2411.17576 v2 pith:4AXMIBXR submitted 2024-11-26 cs.CV

Jovana Videnovic , Alan Lukezic , Matej Kristan This is my paper
classification cs.CV
keywords sam2memorymodelobjecttrackerstrackingbenchmarksdistractor-aware
verification ladder T0 review T1 audit T2 compute T3 formal
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Memory-based trackers are video object segmentation methods that form the target model by concatenating recently tracked frames into a memory buffer and localize the target by attending the current image to the buffered frames. While already achieving top performance on many benchmarks, it was the recent release of SAM2 that placed memory-based trackers into focus of the visual object tracking community. Nevertheless, modern trackers still struggle in the presence of distractors. We argue that a more sophisticated memory model is required, and propose a new distractor-aware memory model for SAM2 and an introspection-based update strategy that jointly addresses the segmentation accuracy as well as tracking robustness. The resulting tracker is denoted as SAM2.1++. We also propose a new distractor-distilled DiDi dataset to study the distractor problem better. SAM2.1++ outperforms SAM2.1 and related SAM memory extensions on seven benchmarks and sets a solid new state-of-the-art on six of them.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SAMITE: Position Prompted SAM2 with Calibrated Memory for Visual Object Tracking

    cs.CV 2025-07 conditional novelty 6.0 of 10

    SAMITE improves zero-shot visual object tracking by selecting trustworthy past frames via prototype similarity and adding positional mask prompts, outperforming prior SAM2-based trackers on most benchmarks.

  2. SAM2RL: Towards Reinforcement Learning Memory Control in Segment Anything Model 2

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A reinforcement learning agent that controls SAM 2 memory bank updates achieves a +4.91% tracking quality gain over SAM 2 when overfitted per video, indicating untapped potential in memory control.

  3. FreeVPS: Repurposing Training-Free SAM2 for Generalizable Video Polyp Segmentation

    cs.CV 2025-08 conditional novelty 5.0 of 10

    FreeVPS pairs a per-frame polyp segmenter with frozen SAM2 tracking and two filtering modules to reduce error accumulation, improving in-domain and out-of-domain video polyp segmentation.

  4. Memory-Augmented SAM2 for Training-Free Surgical Video Segmentation

    cs.CV 2025-07 conditional novelty 5.0 of 10

    MA-SAM2 adds context-aware and occlusion-resilient memory to SAM2 and reports Challenge IoU of 62.49 percent on EndoVis2017 and 64.40 percent on EndoVis2018, beating SAM2 by 6.10 and 4.36 points.

  5. THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation

    cs.CV 2025-06 conditional novelty 4.0 of 10

    On the EPIC-KITCHENS VISOR test set, the proposed Cutie-based egocentric video object segmentation method with SAM2-pretrained Hiera-L and Depth Anything V2 fusion reports a J&F score of 90.1%.

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