Labeling a large set of data is expensive. Active learning aims to tackle this problem by
asking to annotate only the most informative data from the unlabeled set. We propose a …

Achieving a reliable LiDAR-based object detector in autonomous driving is paramount, but
its success hinges on obtaining large amounts of precise 3D annotations. Active learning …

The objective of Active Learning is to strategically label a subset of the dataset to maximize
performance within a predetermined labeling budget. In this study, we harness features …

To alleviate the high annotation cost in LiDAR-based 3D object detection, active learning is
a promising solution that learns to select only a small portion of unlabeled data to annotate …

Significant strides have been made in closed world 3D object detection, testing systems in
environments with known classes. However, the challenge arises in open world scenarios …

Active learning (AL) is designed to construct a high-quality labeled dataset by iteratively
selecting the most informative samples. Such sampling heavily relies on data …

As the amount of labeled data increases, the performance of deep neural networks tends to
improve. However, annotating a large volume of data can be expensive. Active learning …

Foot-and-mouth disease poses a significant threat to both domestic and wild cloven-hoofed
animals, leading to severe economic losses and jeopardizing food security. While machine …

Deep neural networks have demonstrated exceptional performance across numerous
applications. However, DNNs require large amounts of labeled data to avoid overfitting …

Active learning (AL) has shown effectiveness in supervised learning studies in computer
vision (CV), while its integration with self-supervised learning (SSL) remains underexplored …