Studies show lots of advanced research on various data types such as image, speech, and
text using deep learning techniques, but nowadays, research on video processing is also an …

Accurate and robust visual object tracking is one of the most challenging and fundamental
computer vision problems. It entails estimating the trajectory of the target in an image …

We propose an unsupervised visual tracking method in this paper. Different from existing
approaches using extensive annotated data for supervised learning, our CNN model is …

Abstract In recent years, Discriminative Correlation Filter (DCF) based methods have
significantly advanced the state-of-the-art in tracking. However, in the pursuit of ever …

In recent years, many tracking algorithms achieve impressive performance via fusing
multiple types of features, however, most of them fail to fully explore the context among the …

Federated Learning is a novel framework that allows multiple devices or institutions to train a
machine learning model collaboratively while preserving their data private. This …

Abstract Multi-modal Large Language Models (MLLMs) have demonstrated their ability to
perceive objects in still images, but their application in video-related tasks, such as object …

Single-object tracking is a well-known and challenging research topic in computer vision.
Over the last two decades, numerous researchers have proposed various algorithms to …

In recent years, discriminative correlation filters based trackers have shown dominant results
for visual object tracking. Combining the online learning efficiency of the correlation filters …

Although numerous recent tracking approaches have made tremendous advances in the
last decade, achieving high-performance visual tracking remains a challenge. In this paper …