Object detection, as of one the most fundamental and challenging problems in computer
vision, has received great attention in recent years. Over the past two decades, we have …

In this paper, we present a comprehensive review of the imbalance problems in object
detection. To analyze the problems in a systematic manner, we introduce a problem-based …

Accurately ranking the vast number of candidate detections is crucial for dense object
detectors to achieve high performance. Prior work uses the classification score or a …

Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled
source domain to a different unlabeled target domain. Most existing UDA methods focus on …

Object detection has been dominated by anchor-based detectors for several years.
Recently, anchor-free detectors have become popular due to the proposal of FPN and Focal …

Although two-stage object detectors have continuously advanced the state-of-the-art
performance in recent years, the training process itself is far from crystal. In this work, we first …

Two head structures (ie fully connected head and convolution head) have been widely used
in R-CNN based detectors for classification and localization tasks. However, there is a lack …

Detecting objects remains one of computer vision and image understanding applications'
most fundamental and challenging aspects. Significant advances in object detection have …

The prevailing framework for solving referring expression grounding is based on a two-stage
process: 1) detecting proposals with an object detector and 2) grounding the referent to one …

The classification and regression head are both indispensable components to build up a
dense object detector, which are usually supervised by the same training samples and thus …