The virtual-to-real paradigm, ie, training models on virtual data and then applying them to
solve real-world problems, has attracted more and more attention from various domains by …

Development of computer vision algorithms using convolutional neural networks and deep
learning has necessitated ever greater amounts of annotated and labelled data to produce …

Data diversity and volume are crucial to the success of training deep learning models, while
in the medical imaging field, the difficulty and cost of data collection and annotation are …

High-quality structured data with rich annotations are critical components in intelligent
vehicle systems dealing with road scenes. However, data curation and annotation require …

Sim2Real domain adaptation (DA) research focuses on the constrained setting of adapting
from a labeled synthetic source domain to an unlabeled or sparsely labeled real target …

The field of validation and artificial intelligence (AI) for automated driving has been a rapidly
emerging field of research and development in the last few years. Despite the enormous …

The assessment of visual blockages in cross-drainage hydraulic structures, such as culverts
and bridges, is crucial for ensuring their efficient functioning and preventing flash flooding …

The task of multimodal relation extraction has attracted significant research attention, but
progress is constrained by the scarcity of available training data. One natural thought is to …

Large and diverse datasets can now be simulated with associated truth to train and evaluate
AI/ML algorithms. This convergence of readily accessible simulation (SIM) tools, real-time …

Synthetic data plays a crucial role in augmenting limited or challenging datasets. One
domain with a scarcity of publicly available datasets is environmental monitoring of smoke …