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

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 …

We introduce CoTracker, a transformer-based model that tracks a large number of 2D points
in long video sequences. Differently from most existing approaches that track points …

In video object tracking, there exist rich temporal contexts among successive frames, which
have been largely overlooked in existing trackers. In this work, we bridge the individual …

We propose PD-GAN, a probabilistic diverse GAN forimage inpainting. Given an input image
with arbitrary holeregions, PD-GAN produces multiple inpainting results withdiverse and …

MoCo is effective for unsupervised image representation learning. In this paper, we propose
VideoMoCo for unsupervised video representation learning. Given a video sequence as an …

By decomposing the visual tracking task into two subproblems as classification for pixel
category and regression for object bounding box at this pixel, we propose a novel fully …

Universal style transfer retains styles from reference images in content images. While
existing methods have achieved state-of-the-art style transfer performance, they are not …

Most existing trackers based on discriminative correlation filters (DCF) try to introduce
predefined regularization term to improve the learning of target objects, eg, by suppressing …

Current state-of-the-art trackers rely only on a target appearance model in order to localize
the object in each frame. Such approaches are however prone to fail in case of eg fast …