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
SSL: A Self-similarity Loss for Improving Generative Image Super-resolution
Du Chen, Zhengqiang Zhang, Jie Liang, Lei Zhang
Performance Evaluation of YOLOv8 Model Configurations, for Instance Segmentation of Strawberry Fruit Development Stages in an Open Field Environment
Abdul-Razak Alhassan Gamani, Ibrahim Arhin, Adrena Kyeremateng Asamoah
PaveCap: The First Multimodal Framework for Comprehensive Pavement Condition Assessment with Dense Captioning and PCI Estimation
Blessing Agyei Kyem, Eugene Kofi Okrah Denteh, Joshua Kofi Asamoah, Armstrong Aboah
MS-Mapping: An Uncertainty-Aware Large-Scale Multi-Session LiDAR Mapping System
Xiangcheng Hu, Jin Wu, Jianhao Jiao, Binqian Jiang, Wei Zhang, Wenshuo Wang, Ping Tan