Ground Truth
"Ground truth" refers to the accurate, verifiable data used to train and evaluate machine learning models. Current research focuses on addressing challenges arising from incomplete, noisy, or changing ground truth data, employing techniques like robust loss functions, self-supervised learning, and data augmentation to improve model accuracy and reliability. These advancements are crucial for various applications, including medical image analysis, autonomous driving, and remote sensing, where obtaining perfect ground truth is often impractical or impossible, impacting the development of robust and reliable AI systems. The development of novel methods for handling imperfect ground truth is a significant area of ongoing research, driving improvements in model performance and generalization across diverse domains.
Papers
Predicting household socioeconomic position in Mozambique using satellite and household imagery
Carles Milà, Teodimiro Matsena, Edgar Jamisse, Jovito Nunes, Quique Bassat, Paula Petrone, Elisa Sicuri, Charfudin Sacoor, Cathryn Tonne
Surprisingly Popular Voting for Concentric Rank-Order Models
Hadi Hosseini, Debmalya Mandal, Amrit Puhan
Learning for Deformable Linear Object Insertion Leveraging Flexibility Estimation from Visual Cues
Mingen Li, Changhyun Choi
Legitimate ground-truth-free metrics for deep uncertainty classification scoring
Arthur Pignet, Chiara Regniez, John Klein
SCRREAM : SCan, Register, REnder And Map:A Framework for Annotating Accurate and Dense 3D Indoor Scenes with a Benchmark
HyunJun Jung, Weihang Li, Shun-Cheng Wu, William Bittner, Nikolas Brasch, Jifei Song, Eduardo Pérez-Pellitero, Zhensong Zhang, Arthur Moreau, Nassir Navab, Benjamin Busam