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Hand Shape Recognition Using Very Deep Convolutional Neural Networks
Zitatschlüssel rakowski-2018-icccv
Autor Rakowski, Alexander and Wandzik, Lukasz
Buchtitel Proceedings of the 2018 International Conference on Control and Computer Vision
Seiten 8–12
Jahr 2018
ISBN 978-1-4503-6470-6
DOI 10.1145/3232651.3232657
Ort Singapore, Singapore
Adresse New York, NY, USA
Verlag ACM
Serie ICCCV '18
Zusammenfassung This work examines the application of modern deep convolutional neural network architectures for classification tasks in the sign language domain. Transfer learning is performed by pre-training the models on the ImageNet dataset. After fine-tuning on the ASL fingerspelling and the 1 Million Hands datasets the models outperform state-of-the-art approaches on both hand shape classification tasks. Introspection of the trained models using Saliency Maps is also performed to analyze how the networks make their decisions. Finally, their robustness is investigated by occluding selected image regions.
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