TAPTRv2: Attention-based Position Update Improves Tracking Any Point
Authors: name: Hongyang Li, name: Hao Zhang, name: Shilong Liu, name: Zhaoyang Zeng, name: Feng Li, name: Tianhe Ren, name: Bohan Li, name: Lei Zhang

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