翻译：Traditional Tracking Framework. The current popular
trackers(Bertinetto et al. 2016; Li et al. 2019; Chen et al.
2021; Ye et al. 2022) are dominated by the Siamese tracking
paradigm, which achieves tracking by image-pair matching.
To improve the accuracy and robustness of trackers, several
 different approaches are proposed, such as prediction
head networks (Li et al. 2018; Chen et al. 2020; Zhang et al.
2020), cross-correlation modules (Han et al. 2021; Liao et al.
2020; Chen et al. 2021), powerful backbone (Chen et al.
2022; Cui et al. 2022) and attention mechanisms (Guo et al.
2021; Yu et al. 2020). In recent years, the introduction of
the transformer (Vaswani et al. 2017) enables trackers (Yan
et al. 2021a; Xie et al. 2022; Cui et al. 2022; Ye et al. 2022)
to explore more powerful and deeper feature interactions, resulting
 in signiffcant advances in tracking algorithm development.
 However, most of these methods are designed based
on offfine mode and sparse image-pair strategy. With this
design paradigm, the tracker struggles to accurately comprehend
 the object’s motion state in the temporal dimension
and can only resort to traditional Siamese similarity for appearance
 modeling. In contrast to these approaches, we reformulate
 object tracking as a token sequence propagation
task and aim to extend Siamese tracker to efffciently exploit
target temporal information in an auto-regressive manner.
Temporal Modelling in Visual Tracking. Multi-object
tracking algorithms(Meinhardt et al. 2022; Zeng et al. 2022)
typically involve the recognition and association of individual
 objects in a video, making the study of trajectory information
 a common practice. However, there exists a relatively
 limited amount of research exploring the utilization
of spatio-temporal trajectory information in single-object
tracking algorithms.
To explore temporal cues within the Siamese framework,
several online update methods are carefully designed. UpdateNet(Zhang
 et al. 2019) introduces an adaptive updating
strategy, which utilizes a custom network to fuse accumulated
 templates and generate a weighted updated template
feature for visual tracking. DCF-based trackers(Danelljan
et al. 2019; Bhat et al. 2019; Danelljan, Gool, and Timofte
2020) excel at updating model parameters online using sophisticated
 optimization techniques, thereby improving the
robustness of the tracker. STMTrack(Fu et al. 2021) and
TrDiMP(Wang et al. 2021a) employ attention mechanism to
effectively extract contextual information along the temporal
dimension. STARK(Yan et al. 2021a) and Mixformer(Cui
et al. 2022) speciffcally design target quality branch for updating
 template frame, which aids in improving the tracking
results. Recently, there has been a gradual surge in research
attention towards modeling temporal context from various
perspectives. TCTrack (Cao et al. 2022) introduces an online
 temporal adaptive convolution and an adaptive temporal
transformer that aggregates temporal contexts at two levels
containing feature extraction and similarity map reffnement.
VideoTrack (Xie et al. 2023) designs a new tracker based on
video transformer and uses a simple feedforward network to
encode temporal dependencies. ARTrack (Xing et al. 2023)
presents a new time-autoregressive tracker that estimates the
coordinate sequence of an object progressively.
Nevertheless, the above tracking algorithms still suffer
from the following limitations: (1) The optimization process
is complex, involving the design of specialized loss functions(Bhat
 et al. 2019), multi-stage training strategies(Yan