Visual object tracking is a fundamental research topic with a broad range of applications.
Benefiting from the rapid development of Transformer, pure Transformer trackers have …

Transformer trackers have achieved impressive advancements recently, where the attention
mechanism plays an important role. However, the independent correlation computation in …

We propose a new Group Feature Selection method for Discriminative Correlation Filters
(GFS-DCF) based visual object tracking. The key innovation of the proposed method is to …

Due to their excellent performance on aggregating global features, Transformer structures
are being widely employed in deep learning-based visual object tracking algorithms …

Existing Siamese or transformer trackers commonly pose visual object tracking as a one-
shot detection problem ie locating the target object in a single forward evaluation scheme …

The design of more complex and powerful neural network models has significantly
advanced the state-of-the-art in visual object tracking. These advances can be attributed to …

Transformer-based trackers have demonstrated promising performance in visual object
tracking tasks. Nevertheless, two drawbacks limited the potential performance improvement …

Transformer architecture has been showing its great strength in visual object tracking, for its
effective attention mechanism. Existing transformer-based approaches adopt the pixel-to …

Visual object tracking aims to precisely estimate the bounding box for the given target, which
is a challenging problem due to factors such as deformation and occlusion. Many recent …

Transformers have been successfully applied to the visual tracking task and significantly
promote tracking performance. The self-attention mechanism designed to model long-range …