TAPTRv2: Attention-based Position Update Improves Tracking Any Point
Hongyang Li, Hao Zhang, Shilong Liu, Zhaoyang Zeng, Feng Li, Tianhe Ren, Bohan Li, Lei Zhang
2024-07-30
Summary
This paper presents TAPTRv2, an improved method for tracking points in motion using a Transformer-based approach. It enhances the original TAPTR model by introducing a new way to update the positions of tracking points, making the tracking process more accurate and efficient.
What's the problem?
Tracking moving points accurately is challenging, especially when relying on complex computations called cost-volumes. These can distort the information about the points being tracked, leading to poor predictions about their visibility and position. The existing TAPTR model faced issues due to this reliance on cost-volume, which negatively affected its performance.
What's the solution?
To address these challenges, TAPTRv2 introduces an attention-based position update (APU) operation. This new method uses attention weights to better combine information from different positions, allowing the model to predict new positions for tracking points more accurately. By eliminating the need for cost-volume calculations, TAPTRv2 improves both the speed and accuracy of tracking. The model was tested and showed better performance than its predecessor, TAPTR, achieving state-of-the-art results on various datasets.
Why it matters?
This research is important because it advances the field of motion tracking, which has applications in areas like robotics, animation, and computer vision. By improving how models track points in motion, TAPTRv2 can lead to more reliable and efficient systems that can be used in real-time scenarios, enhancing technologies that rely on accurate motion detection.
Abstract
In this paper, we present TAPTRv2, a Transformer-based approach built upon TAPTR for solving the Tracking Any Point (TAP) task. TAPTR borrows designs from DEtection TRansformer (DETR) and formulates each tracking point as a point query, making it possible to leverage well-studied operations in DETR-like algorithms. TAPTRv2 improves TAPTR by addressing a critical issue regarding its reliance on cost-volume,which contaminates the point query\'s content feature and negatively impacts both visibility prediction and cost-volume computation. In TAPTRv2, we propose a novel attention-based position update (APU) operation and use key-aware deformable attention to realize. For each query, this operation uses key-aware attention weights to combine their corresponding deformable sampling positions to predict a new query position. This design is based on the observation that local attention is essentially the same as cost-volume, both of which are computed by dot-production between a query and its surrounding features. By introducing this new operation, TAPTRv2 not only removes the extra burden of cost-volume computation, but also leads to a substantial performance improvement. TAPTRv2 surpasses TAPTR and achieves state-of-the-art performance on many challenging datasets, demonstrating the superiority