Titlebook: Computer Vision, Imaging and Computer Graphics Theory and Applications; 17th International J A. Augusto de Sousa,Kurt Debattista,Kadi Bouat

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,Active Learning with?Data Augmentation Under Small vs Large Dataset Regimes for?Semantic-KITTI Datae impact over the labeling efficiency and thus the capacity to distill datasets. We demonstrate key issues in designing a functional AL framework and finally conclude with a review of challenges in real world active learning.

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978-3-031-45724-1The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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critique

https://doi.org/10.1007/b102409reo (MVS) or Structure-from-Motion (SfM). To find the best model fitting the data, from numerous minimal samples it evaluates models and rates them according to their number of inliers. The classification as inlier depends on a user-set threshold that should be tailored to the noise level of the dat

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The Scope of Bureaucratic Management,r vehicle trajectory classification. We extend our previous approach to be able to classify a larger number of vehicle trajectories collected from different sources in a single Bi-LSTM network. We also explored the use of deep learning visual explainability by highlighting the parts of the activity