In this paper, we introduce a new large-scale object detection dataset, Objects365, which
has 365 object categories over 600K training images. More than 10 million, high-quality …

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 …

Abstract Knowledge distillation (KD) has witnessed its powerful capability in learning
compact models in object detection. Previous KD methods for object detection mostly focus …

The field of object detection has made great progress in recent years. Most of these
improvements are derived from using a more sophisticated convolutional neural network …

In this study, we partially reannotate conventional benchmark datasets for object detection
and check whether there is performance improvement/drop compared with the original …

We propose a single-stage detection framework that jointly tackles the problem of multi-
scale object detection and class imbalance. Rather than designing deeper networks, we …

Object localization in general environments is a fundamental part of vision systems. While
dominating on the COCO benchmark, recent Transformer-based detection methods are not …

Modern object detectors rely heavily on rectangular bounding boxes, such as anchors,
proposals and the final predictions, to represent objects at various recognition stages. The …

Label assignment (LA), which aims to assign each training sample a positive (pos) and a
negative (neg) loss weight, plays an important role in object detection. Existing LA methods …

State-of-the-art object detectors rely on regressing and classifying an extensive list of
possible anchors, which are divided into positive and negative samples based on their …