In recent years, significant progress has been achieved in urban dense prediction tasks,
particularly with advancements in deep learning models and novel architectures that …

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

RGBT object tracking is widely used due to the complementary nature of RGB and TIR
modalities. However, RGBT trackers based on Transformer or CNN face significant …

Multimodal tracking has garnered widespread attention as a result of its ability to effectively
address the inherent limitations of traditional RGB tracking. However, existing multimodal …

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 …

Event camera-based visual tracking has drawn more and more attention in recent years due
to the unique imaging principle and advantages of low energy consumption, high dynamic …

Visual Object Tracking (VOT) is an attractive and significant research area in computer
vision, which aims to recognize and track specific targets in video sequences where the …

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 …