TAPTRv3: Spatial and Temporal Context Foster Robust Tracking of Any
  Point in Long Video
Authors: name: Jinyuan Qu, name: Hongyang Li, name: Shilong Liu, name: Tianhe Ren, name: Zhaoyang Zeng, name: Lei Zhang

In this paper, we present TAPTRv3, which is built upon TAPTRv2 to improve its
point tracking robustness in long videos. TAPTRv2 is a simple DETR-like
framework that can accurately track any point in real-world videos without
requiring cost-volume. TAPTRv3 improves TAPTRv2 by addressing its shortage in
querying high quality features from long videos, where the target tracking
points normally undergo increasing variation over time. In TAPTRv3, we propose
to utilize both spatial and temporal context to bring better feature querying
along the spatial and temporal dimensions for more robust tracking in long
videos. For better spatial feature querying, we present Context-aware
Cross-Attention (CCA), which leverages surrounding spatial context to enhance
the quality of attention scores when querying image features. For better
temporal feature querying, we introduce Visibility-aware Long-Temporal
Attention (VLTA) to conduct temporal attention to all past frames while
considering their corresponding visibilities, which effectively addresses the
feature drifting problem in TAPTRv2 brought by its RNN-like long-temporal
modeling. TAPTRv3 surpasses TAPTRv2 by a large margin on most of the
challenging datasets and obtains state-of-the-art performance. Even when
compared with methods trained with large-scale extra internal data, TAPTRv3 is
still competitive.