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Two Preprints Accepted for Presentation at VISIGRAPP 2024 in Rome, Italy

07.12.2023|11:28 Uhr

Both papers contribute to the advancement of data-centric artificial intelligence.

[1] „Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection“ by T. Riedlinger, M. Schubert, K. Kahl, H. Gottschalk, M. Rottmann

This work provides a sandbox environment that allows rapid prototyping of active learning methods for object detection, saving up to a factor of 32 in computational time. This is complemented by useful evaluation metrics, baselines for comparison, and baseline results on PascalVOC and BDD100k that can be reused by the community.

Preprint: https://arxiv.org/pdf/2212.10836

 

[2] „Deep Active Learning with Noisy Oracle in Object Detection“ by M. Schubert, T. Riedlinger, K. Kahl, M. Rottmann

During our work on [1], we realized that label quality plays a particularly important role in situations where only small amounts of training data are available. We combine AI-guided label checking with simple uncertainty-guided data selection and show that

1) label checking significantly improves model performance (as a function of labeling effort) and

2) uncertainty-guided data selection and label checking are indeed synergistic.

Preprint: https://arxiv.org/pdf/2310.00372

 

Both papers were conducted as a collaboration between the Technical University of Berlin and the University of Wuppertal.