The rich spatio-temporal information is crucial to capture the complicated target appearance
variations in visual tracking. However most top-performing tracking algorithms rely on many …

Transformer attention plays an important role in current top-performing trackers. However, it
is bottom-up, driven by stimulus and lacks intrinsic prior guidance. This bottom-up attention …

Fully transformer-based one-stream trackers have demonstrated outstanding performance
on challenging benchmark datasets over the past three years. These trackers enable …

Current tracking methods often adopt a compact template to emphasize target-specific
features, alongside an expansive search region to encapsulate surrounding environmental …

Single-object tracking generally advances by incrementally determining the tracked target's
position through interactions between the search region and the template. However, the …

Accurate and efficient ship tracking by geosynchronous orbit (GEO) satellites holds great
significance for large-scale maritime surveillance. Nevertheless, ship tracking continues to …

Transformer-based tracking methods have been widely studied in the field of visual object
tracking. The long-range information capturing ability of the transformer improves the …

Transformer-based visual object tracking surpasses conventional CNN-based counterparts
in superior performance but comes with additional computational overhead. Existing …

Siamese network-based trackers have demonstrated competitive performance in the domain
of single object tracking. However, their effectiveness is significantly hindered when the …

One-stream Transformer trackers have shown outstanding performance in challenging
benchmark datasets over the last three years, as they enable interaction between the target …