Utilizing Dataset Affinity Prediction in Object Detection to Assess Training Data-Reference-Cited by-同舟云学术

Utilizing Dataset Affinity Prediction in Object Detection to Assess Training Data

Published:2024 Issue: Volume: Page:260-277
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:en
Short-container-title:

Author:

Becker Stefan^ORCID,Bayer Jens^ORCID,Hug Ronny^ORCID,Huebner Wolfgang^ORCID,Arens Michael^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-59057-3_17

Reference72 articles.

1. Akyon, F.C., Altinuc, S.O., Temizel, A.: Slicing aided hyper inference and fine-tuning for small object detection. In: 2022 IEEE International Conference on Image Processing (ICIP), pp. 966–970 (2022). https://doi.org/10.1109/ICIP46576.2022.9897990

2. Azevedo, T., de Jong, R., Maji, P.: Stochastic-yolo: efficient probabilistic object detection under dataset shifts. In: NeurIPS Workshop on Machine Learning for Autonomous Driving (ML4AD) (2020)

3. Bayer, J., Münch, D., Arens, M.: APMD: adversarial pixel masking derivative for multispectral object detectors. In: Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies VI, vol. 12275, p. 122750F. International Society for Optics and Photonics, SPIE (2022). https://doi.org/10.1117/12.2637977

4. Bogdoll., D., Schreyer., F., Zöllner., J.M.: Ad-datasets: a meta-collection of data sets for autonomous driving. In: Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS, pp. 46–56. INSTICC, SciTePress (2022). https://doi.org/10.5220/0011001900003191

5. Bogdoll, D., Uhlemeyer, S., Kowol, K., Zöllner, J.M.: Perception datasets for anomaly detection in autonomous driving: a survey (2023). https://doi.org/10.48550/ARXIV.2302.02790, https://arxiv.org/abs/2302.02790