Modality gap between RGB and thermal infrared (TIR) images is a crucial issue but often
overlooked in existing RGBT tracking methods. It can be observed that modality gap mainly …

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

Most RGB-T trackers heavily rely on bottom-up attention and thus overlook top-down cross-
modal guidance for learning target features. Consequently, the discriminative power of the …

In RGB-T tracking, there exist rich spatial relationships between the target and backgrounds
within multi-modal data as well as sound consistencies of spatial relationships among …

Existing Transformer-based RGBT trackers achieve remarkable performance benefits by
leveraging self-attention to extract uni-modal features and cross-attention to enhance multi …

RGB-T tracking, a vital downstream task of object tracking, has made remarkable progress in
recent years. Yet, it remains hindered by two major challenges:(1) the trade-off between …

With the emergence of a single large model capable of successfully solving a multitude of
tasks in NLP, there has been growing research interest in achieving similar goals in …

Multi-modal feature fusion as a core investigative component of RGBT tracking emerges
numerous fusion studies in recent years. However, existing RGBT tracking methods widely …

Existing RGBT tracking methods often design various interaction models to perform cross-
modal fusion of each layer, but can not execute the feature interactions among all layers …

The complementary benefits from visible and thermal infrared data are widely utilized in
various computer vision task, such as visual tracking, semantic segmentation and object …