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 …

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 …

Recent breakthroughs have used deep learning to exploit evolutionary information in
multiple sequence alignments (MSAs) to accurately predict protein structures. However …

False negatives (FN) in 3D object detection, eg, missing predictions of pedestrians, vehicles,
or other obstacles, can lead to potentially dangerous situations in autonomous driving. While …

Abstract 3D object detection is receiving increasing attention from both industry and
academia thanks to its wide applications in various fields. In this paper, we propose Point …

Deep classifiers have achieved great success in visual recognition. However, real-world
data is long-tailed by nature, leading to the mismatch between training and testing …

Deep learning-based object detection and instance segmentation have achieved
unprecedented progress. In this article, we propose complete-IoU (CIoU) loss and Cluster …

Bounding box regression is the crucial step in object detection. In existing methods, while ℓ
n-norm loss is widely adopted for bounding box regression, it is not tailored to the evaluation …

Arbitrary-oriented objects widely appear in natural scenes, aerial photographs, remote
sensing images, etc., and thus arbitrary-oriented object detection has received considerable …

Deep learning has recently unleashed the ability for Machine learning (ML) to make
unparalleled strides. It did so by confronting and successfully addressing, at least to a …