@inproceedings{
li2024taptrv,
title={{TAPTR}v2: Attention-based Position Update Improves Tracking Any Point},
author={Hongyang Li and Hao Zhang and Shilong Liu and Zhaoyang Zeng and Feng Li and Bohan Li and Tianhe Ren and Lei Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=Cx2O6Xz03H}
}
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 effectiveness of our approach.
TAPTRv2: Attention-based Position Update Improves Tracking Any Point
Hongyang Li Hao Zhang Shilong Liu Zhaoyang Zeng Feng Li Bohan Li Tianhe Ren Lei Zhang