Building robust and generic object detection frameworks requires scaling to larger label
spaces and bigger training datasets. However, it is prohibitively costly to acquire annotations …

Recent advances in deep learning have led to the development of accurate and efficient
models for various computer vision applications such as classification, segmentation, and …

Though unsupervised domain adaptation (UDA) has achieved very impressive progress
recently, it remains a great challenge due to missing target annotations and the rich …

The recent detection transformer (DETR) simplifies the object detection pipeline by removing
hand-crafted designs and hyperparameters as employed in conventional two-stage object …

近年来, 基于深度学习的目标检测算法在自动驾驶, 人机交互等众多域上有着成功的应用,
且因其检测性能较高引起学者的广泛关注. 传统的深度学习方法一般基于源域与目标域服从同一 …

Deep learning (DL) based object detection has achieved great progress. These methods
typically assume that large amount of labeled training data is available, and training and test …

This survey paper specially analyzed computer vision-based object detection challenges
and solutions by different techniques. We mainly highlighted object detection by three …

The perception of the environment plays a decisive role in the safe and secure operation of
autonomous vehicles. The perception of the surrounding is way similar to human vision. The …

The recent detection transformer (DETR) simplifies the object detection pipeline by removing
hand-crafted designs and hyperparameters as employed in conventional two-stage object …

Cross-domain object detection uses knowledge from source domain tasks to enhance the
object detection in target domain. It can reduce the workload of data annotations in the new …