Core Concepts
Proposing a simple and strong framework for Tracking Any Point with Transformers (TAPTR) based on DETR-like algorithms.
Abstract
The content introduces TAPTR, a framework for tracking any point using transformers. It borrows designs from DETR-like algorithms, representing each tracking point as a query in video frames. The model demonstrates strong performance on various datasets with faster inference speed. Extensive experiments and ablation studies are conducted to validate the effectiveness of key components.
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Introduction
- Importance of pixel tracking in computer vision.
- Evolution from optical flow estimation to key-point tracking to TAP task.
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Related Work
- Overview of optical flow methods and recent works addressing TAP tasks.
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TAPTR Model
- Task definition and overview.
- Video preparation, query preparation, point decoder, window post-processing explained.
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Experiments
- Training details, dataset information, evaluation protocol, metrics discussed.
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Comparison with State of the Art
- Evaluation of TAPTR against previous methods on TAP-Vid benchmark.
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Ablation Studies
- Impact of key components like self-attention, cost volume aggregation, etc., analyzed through ablation studies.
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Visualization
- Trajectory prediction and video editing results demonstrated visually.
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Appendix
- Additional information on BADJA benchmark performance, trajectory prediction examples, video editing results provided.
Stats
Our model surpasses CoTracker on DAVIS dataset (63.0 vs 60.7).
Achieves state-of-the-art performance with faster inference speed.
Quotes
"In this paper, we propose a simple and strong framework for Tracking Any Point with Transformers (TAPTR)."
"Our framework demonstrates strong performance with state-of-the-art performance on various TAP datasets."