Post-graduate theses
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Identifier |
000462979 |
Title |
A commonsense knowledge-driven framework for part-of relation discovery in images |
Alternative Title |
Μεθοδολογία για τον εντοπισμό σχέσεων μέρους-όλου σε εικόνες αξιοποιώντας γνώση κοινής λογικής |
Author
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Κουμούρου, Σωτήρης Θ.
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Thesis advisor
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Πλεξουσάκης, Δημήτριος
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Reviewer
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Πάτκος, Θοδωρής
Ευθυμίου, Βασίλης
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Abstract |
The integration of data-driven techniques with knowledge-based methods has led to
many accomplishments in the past few years. While machine learning techniques have
proven their potential in diverse domains, they often display weaknesses that symbolic
methods can help overcome.
This thesis focuses on the development of a framework tailored for the detection
of partOf relations in images through the integration of commonsense knowledge. A
partOf relation represents the association between an entity and its component or constituent part. The primary objective of this framework is to enhance the precision of
relation predictions by incorporating external information derived from problem-agnostic
knowledge graphs, specifically ConceptNet. The proposed framework, named PReDeCK,
relies on graphs for capturing structured, semantic knowledge about commonsense notions. By conducting symbolic reasoning over the extracted information utilizing the expressive capabilities offered by Answer Set Programming (ASP), PReDeCK can eliminate
counter-intuitive conclusions and improve the accuracy of relation predictions.
Additionally, this thesis addresses the challenge of identifying errors in the outputs
generated by object detection models. We expand PReDeCK to detect potential errors by
cross-referencing the model’s results with established real-world knowledge. Moreover,
techniques for addressing identified errors are also outlined.
We perform experimental evaluations to assess the performance of the framework
by using a well-known image dataset, Semantic Pascal-Part, which includes a diverse
range of everyday objects and their constituent parts. Throughout the experimental phase,
we analyze various framework variations to highlight the critical role of a robust and
accurate knowledge domain, as well as the impact of incomplete or erroneous data on
the results. The outcomes of these experiments verify that the synergy between visual
data and commonsense knowledge leads to a significant improvement in the precision of
relation detection. Additionally, the results regarding the error detection task, are quite
promising, marking an initial step in quality assurance for object detection models.
Overall, this thesis provides insights into a commonsense knowledge-driven framework for discovering partOf relations in images and for identifying potential errors in
the outputs generated by an object detection model through the use of symbolic reasoning.
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Language |
English |
Subject |
Commonsense knowledge |
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Computer vision |
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Neural networks |
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Neurosymbolic AI |
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Reasoning |
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Γνώση κοινής λογικής |
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Νευρωνικά δίκτυα |
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Συλλογιστική |
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Υπολογιστική όραση |
Issue date |
2024-03-22 |
Collection
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School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
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Type of Work--Post-graduate theses
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Permanent Link |
https://elocus.lib.uoc.gr//dlib/f/0/d/metadata-dlib-1709283329-15283-7120.tkl
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Views |
135 |