Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-18357
Title: Efficient deep CNNs for cross-modal automated computer vision under time and space constraints
Authors : Amirian, Mohammadreza
Rombach, Katharina
Tuggener, Lukas
Schilling, Frank-Peter
Stadelmann, Thilo
et. al : No
Conference details: ECML-PKDD 2019, Würzburg, Germany, 16 - 19 September 2019
Publisher / Ed. Institution : ZHAW Zürcher Hochschule für Angewandte Wissenschaften
Issue Date: 2019
License (according to publishing contract) : Licence according to publishing contract
Type of review: No review
Language : English
Subjects : AutoDL; Automated deep learning; Convolutional neural networks; MobileNetV2; EfficientNet
Subject (DDC) : 004: Computer science
Abstract: We present an automated computer vision architecture to handle video and image data using the same backbone networks. We show empirical results that lead us to adopt MOBILENETV2 as this backbone architecture. The paper demonstrates that neural architectures are transferable from images to videos through suitable preprocessing and temporal information fusion.
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Publication type: Conference paper
DOI : 10.21256/zhaw-18357
URI: https://digitalcollection.zhaw.ch/handle/11475/18357
Published as part of the ZHAW project : Ada – Advanced Algorithms for an Artificial Data Analyst
Appears in Collections:Publikationen School of Engineering

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