Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-18357Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Amirian, Mohammadreza | - |
| dc.contributor.author | Rombach, Katharina | - |
| dc.contributor.author | Tuggener, Lukas | - |
| dc.contributor.author | Schilling, Frank-Peter | - |
| dc.contributor.author | Stadelmann, Thilo | - |
| dc.date.accessioned | 2019-10-04T14:11:14Z | - |
| dc.date.available | 2019-10-04T14:11:14Z | - |
| dc.date.issued | 2019 | - |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/18357 | - |
| dc.description.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. | de_CH |
| dc.language.iso | en | de_CH |
| dc.publisher | ZHAW Zürcher Hochschule für Angewandte Wissenschaften | de_CH |
| dc.rights | Licence according to publishing contract | de_CH |
| dc.subject | AutoDL | de_CH |
| dc.subject | Automated deep learning | de_CH |
| dc.subject | Convolutional neural networks | de_CH |
| dc.subject | MobileNetV2 | de_CH |
| dc.subject | EfficientNet | de_CH |
| dc.subject.ddc | 006: Spezielle Computerverfahren | de_CH |
| dc.title | Efficient deep CNNs for cross-modal automated computer vision under time and space constraints | de_CH |
| dc.type | Konferenz: Paper | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | School of Engineering | de_CH |
| zhaw.organisationalunit | Institut für Informatik (InIT) | de_CH |
| dc.identifier.doi | 10.21256/zhaw-18357 | - |
| zhaw.conference.details | ECML-PKDD 2019, Würzburg, Germany, 16-19 September 2019 | de_CH |
| zhaw.funding.eu | No | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.publication.status | acceptedVersion | de_CH |
| zhaw.publication.review | Keine Begutachtung | de_CH |
| zhaw.webfeed | Datalab | de_CH |
| zhaw.webfeed | Information Engineering | de_CH |
| zhaw.webfeed | ZHAW digital | de_CH |
| zhaw.webfeed | Natural Language Processing | de_CH |
| zhaw.webfeed | Machine Perception and Cognition | de_CH |
| zhaw.webfeed | Intelligent Vision Systems | de_CH |
| zhaw.funding.zhaw | Ada – Advanced Algorithms for an Artificial Data Analyst | de_CH |
| zhaw.author.additional | No | de_CH |
| Appears in collections: | Publikationen School of Engineering | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2019_Stadelmann_Efficient Deep CNNS_ECML_PKDD_2019.pdf | 168.59 kB | Adobe PDF |  View/Open |
Show simple item record
Amirian, M., Rombach, K., Tuggener, L., Schilling, F.-P., & Stadelmann, T. (2019). Efficient deep CNNs for cross-modal automated computer vision under time and space constraints. ECML-PKDD 2019, Würzburg, Germany, 16-19 September 2019. https://doi.org/10.21256/zhaw-18357
Amirian, M. et al. (2019) ‘Efficient deep CNNs for cross-modal automated computer vision under time and space constraints’, in ECML-PKDD 2019, Würzburg, Germany, 16-19 September 2019. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. Available at: https://doi.org/10.21256/zhaw-18357.
M. Amirian, K. Rombach, L. Tuggener, F.-P. Schilling, and T. Stadelmann, “Efficient deep CNNs for cross-modal automated computer vision under time and space constraints,” in ECML-PKDD 2019, Würzburg, Germany, 16-19 September 2019, 2019. doi: 10.21256/zhaw-18357.
AMIRIAN, Mohammadreza, Katharina ROMBACH, Lukas TUGGENER, Frank-Peter SCHILLING und Thilo STADELMANN, 2019. Efficient deep CNNs for cross-modal automated computer vision under time and space constraints. In: ECML-PKDD 2019, Würzburg, Germany, 16-19 September 2019. Conference paper. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. 2019
Amirian, Mohammadreza, Katharina Rombach, Lukas Tuggener, Frank-Peter Schilling, and Thilo Stadelmann. 2019. “Efficient Deep CNNs for Cross-Modal Automated Computer Vision under Time and Space Constraints.” Conference paper. In ECML-PKDD 2019, Würzburg, Germany, 16-19 September 2019. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-18357.
Amirian, Mohammadreza, et al. “Efficient Deep CNNs for Cross-Modal Automated Computer Vision under Time and Space Constraints.” ECML-PKDD 2019, Würzburg, Germany, 16-19 September 2019, ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 2019, https://doi.org/10.21256/zhaw-18357.
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