Object detection and tracking is one of the most important and challenging branches in
computer vision, and have been widely applied in various fields, such as health-care …

Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

Exploiting a general-purpose neural architecture to replace hand-wired designs or inductive
biases has recently drawn extensive interest. However, existing tracking approaches rely on …

Correlation acts as a critical role in the tracking field, especially in recent popular Siamese-
based trackers. The correlation operation is a simple fusion manner to consider the similarity …

In this paper, we present a new tracking architecture with an encoder-decoder transformer
as the key component. The encoder models the global spatio-temporal feature …

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 …

In this paper, we present LaSOT, a high-quality benchmark for Large-scale Single Object
Tracking. LaSOT consists of 1,400 sequences with more than 3.5 M frames in total. Each …

Tracking by natural language specification is a new rising research topic that aims at
locating the target object in the video sequence based on its language description …

Variation of scales or aspect ratios has been one of the main challenges for tracking. To
overcome this challenge, most existing methods adopt either multi-scale search or anchor …

Visual target tracking is one of the most sought-after yet challenging research topics in
computer vision. Given the ill-posed nature of the problem and its popularity in a broad …