In recent years, visual object tracking, as a prominent research area in computer vision, has
garnered significant attention. To bolster the robustness of trackers across a spectrum of …

Existing RGB-T tracking algorithms have made remarkable progress by leveraging the
global interaction capability and extensive pre-trained models of the Transformer …

How to better fuse cross-modal features is the core issue of RGB-T tracking. Some previous
methods either insufficiently fuse RGB and TIR features, or depend on intermediaries …

The Transformer architectures have recently been attracting increasing attention, owing to
their superiority in performance. However, it is difficult to take advantage of them for …

Multi-modal object tracking has received increasing attention, given the limitations the
representation ability in certain challenging scenarios of single RGB modality. Recent …

Visual object tracking, pivotal for applications like earth observation and environmental
monitoring, encounters challenges under adverse conditions such as low light and complex …

Thermal infrared (TIR) data exhibits higher tolerance to extreme environments, making it a
valuable complement to RGB data in tracking tasks. RGBT tracking aims to leverage …

Supervised RGBT (SRGBT) tracking tasks need both expensive and time-consuming
annotations. Therefore, the implementation of Self-Supervised RGBT (SSRGBT) tracking …

Many state-of-the-art RGB-T trackers have achieved remarkable results through modality
fusion. However, these trackers often either overlook temporal information or fail to fully …

RGBT tracking aims to take full advantage of the complementary advantages of RGB and
Thermal Infrared (TIR) modalities to achieve robust tracking in complex scenes. However …