3 accepted papers
Roadside perception datasets are typically constructed via cooperative labeling between synchronized vehicle and roadside frame pairs, but real deployment is often limited roadside-only data due to hardware and privacy constraints. The observation that even human experts struggle to produce accurate
Active learning, a method to reduce labeling effort for training deep neural networks, is often limited by the assumption that all unlabeled data belong to known classes. This closed-world assumption fails in practical scenarios with unknown classes in the data, leading to active open-set annotation…
3D object detection is fundamentally important for various emerging applications, including autonomous driving and robotics. A key requirement for training an accurate 3D object detector is the availability of a large amount of LiDAR-based point cloud data. Unfortunately, labeling point cloud data i…