Active learning (AL) is to design label-efficient algorithms by labeling the most
representative samples. It reduces annotation cost and attracts increasing attention from the …

3D object detection has recently received much attention due to its great potential in
autonomous vehicle (AV). The success of deep learning based object detectors relies on the …

Deep active learning has emerged as a powerful tool for training deep learning models
within a predefined labeling budget. These models have achieved performances …

We propose SeedAL, a method to seed active learning for efficient annotation of 3D point
clouds for semantic segmentation. Active Learning (AL) iteratively selects relevant data …

One way to improve annotation efficiency is active learning. The goal of active learning is to
select images from many unlabeled images, where labeling will improve the accuracy of the …

Adversarial attacks in computer vision exploit the vulnerabilities of machine learning models
by introducing subtle perturbations to input data, often leading to incorrect predictions or …

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

Active learning strategically selects informative unlabeled data points and queries their
ground truth labels for model training. The prevailing assumption underlying this machine …

Medical image classification is the foundational task in medical image analysis which
depends on the high-quality annotated training data. Due to the high cost of medical image …

Rapid development of hardware goes hand-in-hand with the advancement of modern
computer vision (CV) algorithms. In a typical machine learning operations (MLOps) flow, this …