We introduce PointOdyssey, a large-scale synthetic dataset, and data generation framework,
for the training and evaluation of long-term fine-grained tracking algorithms. Our goal is to …

Transformer-based visual trackers have demonstrated significant progress owing to their
superior modeling capabilities. However, existing trackers are hampered by low speed …

Transformer-based trackers have achieved strong accuracy on the standard benchmarks.
However, their efficiency remains an obstacle to practical deployment on both GPU and …

In this article, we provide an intuitive viewing to simplify the Siamese-based trackers by
converting the tracking task to a classification. Under this viewing, we perform an in-depth …

Recently, the transformer has enabled the speed-oriented trackers to approach state-of-the-
art (SOTA) performance with high-speed thanks to the smaller input size or the lighter …

The deployment of transformers for visual object tracking has shown state-of-the-art results
on several benchmarks. However, the transformer-based models are under-utilized for …

The recent advancements in transformer-based visual trackers have led to significant
progress, attributed to their strong modeling capabilities. However, as performance …

Deep learning algorithms provide visual tracking robustness at an unprecedented level, but
realizing an acceptable performance is still challenging because of the natural continuous …

Addressing the vulnerability of deep neural networks (DNNs) has attracted significant
attention in recent years. While recent studies on adversarial attack and defense mainly …

Hyperspectral video target tracking is generally challenging when the scale of the target
varies. In this paper, a novel algorithm is proposed to address the challenges prevalent in …