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 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 …

DETR has been recently proposed to eliminate the need for many hand-designed
components in object detection while demonstrating good performance. However, it suffers …

Abstract Model efficiency has become increasingly important in computer vision. In this
paper, we systematically study neural network architecture design choices for object …

High-resolution representations are essential for position-sensitive vision problems, such as
human pose estimation, semantic segmentation, and object detection. Existing state-of-the …

This paper revisits feature pyramids networks (FPN) for one-stage detectors and points out
that the success of FPN is due to its divide-and-conquer solution to the optimization problem …

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 …

Object detection, one of the most fundamental and challenging problems in computer vision,
seeks to locate object instances from a large number of predefined categories in natural …

Current state-of-the-art convolutional architectures for object detection are manually
designed. Here we aim to learn a better architecture of feature pyramid network for object …

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 …