In the realm of video object tracking, auxiliary modalities such as depth,
thermal, or event data have emerged as valuable assets to complement the RGB
trackers. In practice, most existing RGB trackers learn a single set of
parameters to use them across datasets and applications. However, a similar
single-model unification for multi-modality tracking presents several
challenges. These challenges stem from the inherent heterogeneity of inputs --
each with modality-specific representations, the scarcity of multi-modal
datasets, and the absence of all the modalities at all times. In this work, we
introduce Un-Track, a \underline{Un}ified Tracker of a single set of parameters
for any modality. To handle any modality, our method learns their common latent
space through low-rank factorization and reconstruction techniques. More
importantly, we use only the RGB-X pairs to learn the common latent space. This
unique shared representation seamlessly binds all modalities together, enabling
effective unification and accommodating any missing modality, all within a
single transformer-based architecture and without the need for
modality-specific fine-tuning. Our Un-Track achieves +8.1 absolute F-score
gain, on the DepthTrack dataset, by introducing only +2.14 (over 21.50) GFLOPs
with +6.6M (over 93M) parameters, through a simple yet efficient prompting
strategy. Extensive comparisons on five benchmark datasets with different
modalities show that Un-Track surpasses both SOTA unified trackers and
modality-specific finetuned counterparts, validating our effectiveness and
practicality.

通过低秩分解和重构技术，我们提出了一种统一跟踪器 Un-Track，该方法通过学习共同的潜在空间来处理任何模态，并且只使用 RGB-X 对来学习共同的表示，在单个基于转换器的体系结构中实现了有效的统一和适应任何缺失的模态，同时无需进行模态特定的微调。在五个具有不同模态的基准数据集上的广泛比较表明，Un-Track 超过了最先进的统一跟踪器和经过模态特定微调的对应物，验证了我们的有效性和实用性。