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 …

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 …

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 …

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 …

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 …

Tracking in the unmanned aerial vehicle (UAV) scenarios is one of the main components of
target-tracking tasks. Different from the target-tracking task in the general scenarios, the …

The training of a feature extraction network typically requires abundant manually annotated
training samples, making this a time-consuming and costly process. Accordingly, we …

In few-shot learning scenarios, the challenge is to generalize and perform well on new
unseen examples when only very few labeled examples are available for each task. Model …

Recently, Siamese networks have drawn great attention in visual tracking community
because of their balanced accuracy and speed. However, features used in most Siamese …