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Reiz, Achim; Albadawi, Mohamad; Sandkuhl, Kurt; Vahl, Matthias; Sidin, Dennis

Towards More Robust Fashion Recognition by Combining Deep-Learning-Based Detection with Semantic Reasoning

2021

Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021)

AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021) <2021, Online>

CEUR Workshop Proceedings, 2846

The company FutureTV produces and distributes self-produced videos in the fashion domain. It creates revenue through the placement of relevant advertising. The placement of apposite ads, though, requires an understanding of the contents of the videos. Until now, this tagging is created manually in a labor-intensive process. We believe that image recognition technologies can significantly decrease the need for manual involvement in the tagging process. However, the tagging of videos comes with additional challenges: Preliminary, new deep-learning models need to be trained on vast amounts of data obtained in a labor-intensive data-collection process. We suggest a new approach for the combining of deep-learning-based recognition with a semantic reasoning engine. Through the explicit declaration of knowledge fitting to the fashion categories present in the training data of the recognition system, we argue that it is possible to refine the recognition results and win extra knowledge beyond what is found in the neural net.

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Sidin, Dennis; Ferrein, Alexander [Betreuender Professor]; Albadawi, Mohamad [Betreuender Mitarbeiter]

Unterwasserdetektion von Munitionsaltlasten auf Sonarbildern durch Convolutional Neural Networks

2019

Rostock, Univ., Bachelor Thesis, 2019

In dieser Bachelorarbeit werden Munitionsaltlasten auf Sonarbildern mit Hilfe von verschiedenen Convolutional Neural Networks detektiert. Dabei wird der Einfluss von Transfer Learning, Data Augmentation und der Variation der Datenmenge untersucht. Zu diesem Zweck werden mehrere neuronale Netze auf Basis von Faster R-CNN und SSD trainiert und evaluiert. Auf Testbildern wird so ein mAP-Wert von 0,763 und ein Recall von 0,897 erreicht.