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Few-shot Object Detection Using Online Random Forests

Digital
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Contributing authors of JOANNEUM RESEARCH:
DI (FH) Werner Bailer; DI Hannes Fassold
Authors
Bailer, Werner; Fassold, Hannes
Abstract:
We propose an approach for few-shot object detection, consisting of a CNN-based generic object detector and feature extractor, and an online random forest as a classifier. This enables incremental training of the classifier, which reaches similar performance with around 20 samples as when using 50+ training samples in batch learning
Title:
Few-shot Object Detection Using Online Random Forests
Seiten:
pp95-97
Publikationsdatum
2020

Publikationsreihe

Proceedings
Proceedings of the Joint Austrian Computer Vision and Robotics Workshop 2020
More files and links
https://diglib.tugraz.at/download.php?id=5f6b1c7fa1ce2&location=browse
DOI-Url:
https://www.doi.org/ 10.3217/978-3-85125-752-6-22
Jahr/Monat:
2020
Bibtex Download

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