In computer vision, significant advances have been made on object detection with the rapid
development of deep convolutional neural networks (CNN). This paper provides a …

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 …

We present a new method that views object detection as a direct set prediction problem. Our
approach streamlines the detection pipeline, effectively removing the need for many hand …

In object detection, determining which anchors to assign as positive or negative samples,
known as anchor assignment, has been revealed as a core procedure that can significantly …

Object detection is one of the most important and challenging branches of computer vision,
which has been widely applied in people's life, such as monitoring security, autonomous …

Object detection is a fundamental visual recognition problem in computer vision and has
been widely studied in the past decades. Visual object detection aims to find objects of …

Compared with model architectures, the training process, which is also crucial to the
success of detectors, has received relatively less attention in object detection. In this work …

The accurate and real-time detection of moving ships has become an essential component
in maritime video surveillance, leading to enhanced traffic safety and security. With the rapid …

With the advent of deep learning, object detection drifted from a bottom-up to a top-down
recognition problem. State of the art algorithms enumerate a near-exhaustive list of object …

Mainstream object detectors based on the fully convolutional network has achieved
impressive performance. While most of them still need a hand-designed non-maximum …