Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …

Metaverse and intelligent transportation system (ITS) are disruptive technologies that have
the potential to transform the current transportation system by decreasing traffic accidents …

Autonomous driving techniques have been flourishing in recent years while thirsting for
huge amounts of high-quality data. However, it is difficult for real-world datasets to keep up …

Black-box unsupervised domain adaptation (UDA) learns with source predictions of target
data without accessing either source data or source models during training, and it has clear …

Abstract Unsupervised Domain Adaptation (UDA) for object detection aims to adapt a model
trained on a source domain to detect instances from a new target domain for which …

In unsupervised domain adaptation (UDA), a target-domain model is trained by the
supervised knowledge from a source domain. Although UDA has recently received much …

Large-scale synthetic traffic image datasets have been widely used to make compensate for
the insufficient data in real world. However, the mismatch in domain distribution between …

Abstract Domain adaptation for Cross-LiDAR 3D detection is challenging due to the large
gap on the raw data representation with disparate point densities and point arrangements …

The anatomical structure detection of fetal cardiac views is crucial for diagnosing fetal
congenital heart disease. In practice there is a large domain gap between different hospitals' …

Abstract Recently, Unsupervised Domain Adaptation (UDA) as one of the transfer learning
can handle the different data distributions and has been utilized in mechanical fault …