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Segment Anything Meets Point Tracking (2307.01197v2)

Published 3 Jul 2023 in cs.CV

Abstract: The Segment Anything Model (SAM) has established itself as a powerful zero-shot image segmentation model, enabled by efficient point-centric annotation and prompt-based models. While click and brush interactions are both well explored in interactive image segmentation, the existing methods on videos focus on mask annotation and propagation. This paper presents SAM-PT, a novel method for point-centric interactive video segmentation, empowered by SAM and long-term point tracking. SAM-PT leverages robust and sparse point selection and propagation techniques for mask generation. Compared to traditional object-centric mask propagation strategies, we uniquely use point propagation to exploit local structure information agnostic to object semantics. We highlight the merits of point-based tracking through direct evaluation on the zero-shot open-world Unidentified Video Objects (UVO) benchmark. Our experiments on popular video object segmentation and multi-object segmentation tracking benchmarks, including DAVIS, YouTube-VOS, and BDD100K, suggest that a point-based segmentation tracker yields better zero-shot performance and efficient interactions. We release our code that integrates different point trackers and video segmentation benchmarks at https://github.com/SysCV/sam-pt.

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Citations (58)
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Summary

  • The paper introduces SAM-PT, a novel zero-shot method combining point-centric segmentation with long-term point tracking.
  • It achieves notable accuracy gains, with improvements of up to 5.0% on DAVIS, 2.0% on YouTube-VOS, and 7.3% on BDD100K.
  • The method reduces labeling overhead by enabling interactive segmentation through sparse, user-guided point tracking across video frames.

Segment Anything Meets Point Tracking: An Expert Analysis

The paper "Segment Anything Meets Point Tracking" introduces an innovative method for interactive video segmentation known as SAM-PT. This approach leverages the capabilities of the Segment Anything Model (SAM), a zero-shot image segmentation model, and integrates it with long-term point tracking methodologies to enhance video segmentation tasks. SAM-PT is particularly notable for its point-centric approach, which deviates from the traditional object-centric mask propagation seen in video segmentation literature.

Technical Overview

SAM-PT extends the functionalities of SAM, a model designed for zero-shot image segmentation using point-centric annotations. By incorporating point trackers, the method allows for sparse point propagation across video frames, thus facilitating interactive segmentation. User interactions, in the form of positive and negative query points, guide the segmentation process, where positive points signify target objects, and negative points denote non-target segments. These points are tracked throughout the video, providing trajectory predictions and occlusion scores. The segmentation masks are generated by prompting SAM with non-occluded points on a per-frame basis.

Strong Numerical Results

The paper highlights significant improvements in segmentation performance using SAM-PT across various benchmarks. Specifically, SAM-PT demonstrated better zero-shot performance on the DAVIS, YouTube-VOS, and BDD100K datasets compared to traditional methods reliant on fully-supervised models like XMem and DeAOT. Notably, SAM-PT achieved up to a 5.0% improvement on DAVIS, a 2.0% increase on YouTube-VOS, and a 7.3% gain on BDD100K in terms of segmentation accuracy. Moreover, on the Unidentified Video Objects (UVO) benchmark, SAM-PT surpassed existing zero-shot and even some fully-supervised video instance segmentation methods by 6.7 points without prior training on video data.

Implications and Future Directions

The introduction of SAM-PT presents several implications for both the theoretical and practical aspects of video segmentation. Theoretically, it challenges the necessity of dense object representations, suggesting that sparse point tracking can be more effective, particularly in zero-shot scenarios. This point-centric method capitalizes on local structure information, offering a perspective that is agnostic to global object semantics.

Practically, SAM-PT's ability to perform competitively without being trained on video segmentation data underscores its flexibility and robustness, presenting significant potential for reducing the data labeling overhead traditionally required in supervised segmentation models. Additionally, its interactive capabilities suggest that SAM-PT could streamline video annotation processes, becoming a viable tool for real-world applications where user intervention is feasible.

Looking ahead, continued advancements in point tracking algorithms could further enhance SAM-PT’s performance, particularly in handling challenges such as occlusions and fast object movements. There's also potential for integrating SAM-PT with existing framework to explore hybrid approaches that leverage both mask and point propagation strategies for even more refined segmentation results.

In summary, this paper offers a substantial contribution to the field of video segmentation by merging foundational image segmentation models with advanced point tracking techniques. SAM-PT not only improves segmentation performance in zero-shot contexts but also opens new avenues for research in interactive and efficient video annotation methodologies.

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  1. GitHub - SysCV/sam-pt: SAM-PT: Extending SAM to zero-shot video segmentation with point-based tracking. (912 stars)
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