Encoding Semantic Attributes - Towards Explainable AI in Industry-Reference-Cited by-同舟云学术

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Encoding Semantic Attributes - Towards Explainable AI in Industry

Published:2023-07-05 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments
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Author:

Schneider Sarah¹^ORCID,Antensteiner Doris²^ORCID,Soukup Daniel²^ORCID,Scheutz Matthias³^ORCID

Affiliation:

1. Department of Computer Science/ School of Engineering/ Human-Robot Interaction Lab, Tufts University, USA and Center for Vision, Automation & Control / High-Performance Vision Systems, Austrian Institute of Technology, Austria

2. Center for Vision, Automation & Control / High-Performance Vision Systems, Austrian Institute of Technology, Austria

3. Department of Computer Science/ School of Engineering/ Human-Robot Interaction Lab, Tufts University, United States

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3594806.3596531

Reference15 articles.

1. David Arthur and Sergei Vassilvitskii . 2007. K-Means++ : The Advantages of Careful Seeding(SODA ’07) . Society for Industrial and Applied Mathematics , USA , 1027–1035. David Arthur and Sergei Vassilvitskii. 2007. K-Means++: The Advantages of Careful Seeding(SODA ’07). Society for Industrial and Applied Mathematics, USA, 1027–1035.

2. Learning and the Unknown: Surveying Steps toward Open World Recognition

3. ImageNet: A large-scale hierarchical image database

4. Supervised and unsupervised learning in vision-guided robotic bin picking applications for mixed-model assembly

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