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Deep Domain Adaptation through Inter-modal Self-supervision

Luca Robbiano

Deep Domain Adaptation through Inter-modal Self-supervision.

Rel. Barbara Caputo, Mirco Planamente, Mohammadreza Loghmani. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2020

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Abstract:

Computer vision in robotics makes heavy usage of RGB-D data. However, collecting large manually annotated datasets is extremely time-consuming and therefore costly. A potential solution is to automatically generate synthetic datasets and to use them in order to make predictions on the real data. Nevertheless, the domain shift between the synthetic dataset (source domain) and the real data (target domain) partially invalidates the effectiveness of this solution, yielding an accuracy significantly lower than the one that would be obtained using labelled real data. In order to overcome this issue, multiple domain adaptation methods have been developed. These methods can also be employed in a multimodal scenario like RGB-D, but none of them exploits the existing relationship between modalities. We propose a novel domain adaptation method which allows reducing the domain shift by forcing the convolutional neural network to learn the connection between RGB and Depth images through a secondary self-supervised task. Extensive experiments on object categorization show that the exploitation of inter-modal relation can significantly enhance the performance of the main classification task.

Relators: Barbara Caputo, Mirco Planamente, Mohammadreza Loghmani
Academic year: 2019/20
Publication type: Electronic
Number of Pages: 61
Subjects:
Corso di laurea: Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering)
Classe di laurea: New organization > Master science > LM-32 - COMPUTER SYSTEMS ENGINEERING
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/14498
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