Abstract Machine learning is driving development across many fields in science and
engineering. A simple and efficient programming language could accelerate applications of …

Deep learning-based intelligent vehicle perception has been developing prominently in
recent years to provide a reliable source for motion planning and decision making in …

摘要目标检测的任务是从图像中精确且高效地识别, 定位出大量预定义类别的物体实例.
随着深度学习的广泛应用, 目标检测的精确度和效率都得到了较大提升, 但基于深度学习的目标 …

In autonomous driving, a LiDAR-based object detector should perform reliably at different
geographic locations and under various weather conditions. While recent 3D detection …

Existing approaches for unsupervised domain adaptive object detection perform feature
alignment via adversarial training. While these methods achieve reasonable improvements …

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 …

Cross-domain object detection is challenging, because object detection model is often
vulnerable to data variance, especially to the considerable domain shift between two …

Most object detection methods for autonomous driving usually assume a onsistent feature
distribution between training and testing data, which is not always the case when weathers …

Object detection typically assumes that training and test samples are drawn from an identical
distribution, which, however, does not always hold in practice. Such a distribution mismatch …

In recent years, deep learning technologies for object detection have made great progress
and have powered the emergence of state-of-the-art models to address object detection …