The tracking-by-detection framework consists of two stages, ie, drawing samples around the
target object in the first stage and classifying each sample as the target object or as …

For visual tracking, an ideal filter learned by the correlation filter (CF) method should take
both discrimination and reliability information. However, existing attempts usually focus on …

The exponential growth of functional magnetic reasoning imaging (fMRI) data offers a great
opportunity for basic and clinical research to explore functional brain activity. Nonetheless …

Underwater visual object tracking is crucial for marine resource exploration and military
security. However, due to the effect of insufficient light and turbid background in underwater …

Single-object tracking, also known as visual tracking, on the drone platform attracts much
attention recently with various applications in computer vision, such as filming and …

Image-based virtual try-on is challenging in fitting a target in-shop clothes onto a reference
person under diverse human poses. Previous works focus on preserving clothing details …

In visual tracking, usually only a small number of samples are labeled, and most existing
deep learning based trackers ignore abundant unlabeled samples that could provide …

Visual tracking aims to estimate object state in a video sequence, which is challenging when
facing drastic appearance changes. Most existing trackers conduct tracking with divided …

Convolutional neural networks (CNNs) have been applied to visual tracking with
demonstrated success in recent years. However, the performance of CNN-based trackers …

Discriminative correlation filter‐(DCF‐) based trackers are computationally efficient and
achieve excellent tracking in challenging applications. However, most of them suffer low …