Abstract RGB-Thermal infrared (RGB-T) image analysis has been actively studied in recent
years. In the past decade, it has received wide attention and made a lot of important …

RGB-thermal (RGBT) tracking has received extensive attention as a research hotspot,
benefiting from the complementarity of visible (RGB) and thermal (T) modalities, which can …

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 …

RGBT object tracking is an important research topic due to the complementary features of
visible and thermal infrared images. However, the established fast-tracking Siamese series …

RGB and thermal source data suffer from both shared and specific challenges, and how to
explore and exploit them plays a critical role in representing the target appearance in RGBT …

RGBT tracking is an attractive topic that benefits from the complementarity of visible and
thermal sensors to better handle tracking tasks in atrocious scenarios. Existing RGBT …

Existing Transformer-based RGB-Thermal (RGBT) tracking methods either use cross-
attention to fuse the two modalities, or use self-attention and cross-attention to model both …

Learning robust multi-modal feature representations is critical for boosting tracking
performance. To this end, we propose a novel X Modality Assisting Network (X-Net) to shed …

The fusion tracking of RGB and thermal infrared image (RGBT) has attracted widespread
interest within target tracking by leveraging the complementing benefits of information from …

RGBT tracking is a challenging task that requires robust fusion of visible images (RGB) and
thermal infrared images (TIR) to handle various scenarios, such as illumination changes …