Multimodal

Tracking Anything in High Quality

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Paper summary

A framework for high-quality tracking-anything in videos combining segmentation and refinement.

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Key points

Two-stage design: Combines a video multi-object segmenter with a pretrained mask refiner model to clean up tracking output.

Mask quality focus: Addresses the common failure mode where trackers lose object boundaries over time, maintaining sharp masks across long clips.

VOTS2023 results: Ranked 2nd place in the VOTS2023 challenge, demonstrating competitive quality against specialized trackers.

Practical tool: Useful for video editing, AR/VR, and content creation pipelines that require pixel-accurate object tracking over long sequences.

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