Object modeling has become a core part of recent tracking frameworks. Current
popular tackers use Transformer attention to extract the template feature
separately or interactively with the search region. However, separate template
learning lacks communication between the template and search regions, which
brings difficulty in extracting discriminative target-oriented features. On the
other hand, interactive template learning produces hybrid template features,
which may introduce potential distractors to the template via the cluttered
search regions. To enjoy the merits of both methods, we propose a robust object
modeling framework for visual tracking (ROMTrack), which simultaneously models
the inherent template and the hybrid template features. As a result, harmful
distractors can be suppressed by combining the inherent features of target
objects with search regions' guidance. Target-related features can also be
extracted using the hybrid template, thus resulting in a more robust object
modeling framework. To further enhance robustness, we present novel variation
tokens to depict the ever-changing appearance of target objects. Variation
tokens are adaptable to object deformation and appearance variations, which can
boost overall performance with negligible computation. Experiments show that
our ROMTrack sets a new state-of-the-art on multiple benchmarks.

提出了一种鲁棒的目标建模框架（ROMTrack），同时对固有模板和混合模板特征进行建模，通过结合目标对象的固有特征和搜索区域的引导来抑制有害干扰物，并使用混合模板提取与目标相关的特征，从而实现更强大的目标建模框架。