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 …

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 …

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 …

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 …

Object tracking is a computer vision task that aims to locate and continuously follow the
movement of an object in video frames, given an initial annotation. Despite its importance …

With the widespread use of deep learning in single object tracking task, mainstream tracking
algorithms treat tracking as a combined classification and regression problem. Classification …

Tracking both location and pose of multiple planar objects (MPOT) is of great significance to
numerous real-world applications. The greater degree-of-freedom of planar objects …

Tiny objects, frequently appearing in practical applications, have weak appearance and
features, and receive increasing interests in many vision tasks, such as object detection and …

The Feature Selection Library (FSLib) introduces a comprehensive suite of feature selection
(FS) algorithms for MATLAB, aimed at improving machine learning and data mining tasks …