Deep learning algorithms have exhibited impressive performance across various computer
vision tasks; however, the challenge of overfitting persists, especially when dealing with …

Data augmentation (DA) is indispensable in modern machine learning and deep neural
networks. The basic idea of DA is to construct new training data to improve the model's …

In-flight system failure is one of the major safety concerns in the operation of unmanned
aerial vehicles (UAVs) in urban environments. To address this concern, a safety framework …

Building instance segmentation models that are data-efficient and can handle rare object
categories is an important challenge in computer vision. Leveraging data augmentations is a …

In order to deal with the sparse and unstructured raw point clouds, most LiDAR based 3D
object detection research focuses on designing dedicated local point aggregators for fine …

Semi-supervised learning (SSL) has a potential to improve the predictive performance of
machine learning models using unlabeled data. Although there has been remarkable recent …

Pyramidal feature representation is the common practice to address the challenge of scale
variation in object detection. However, the inconsistency across different feature scales is a …

LiDAR point clouds, which are usually scanned by rotating LiDAR sensors continuously,
capture precise geometry of the surrounding environment and are crucial to many …

Unsupervised domain adaptive person re-identification (UDA re-ID) aims at transferring the
labeled source domain's knowledge to improve the model's discriminability on the unlabeled …

In the past decade, differential privacy has seen remarkable success as a rigorous and
practical formalization of data privacy. This privacy definition and its divergence based …