A Neuro-Symbolic ASP Pipeline for Visual Question Answering

We present a neuro-symbolic visual question answering (VQA) pipeline for CLEVR, which is a well-known dataset that consists of pictures showing scenes with objects and questions related to them. Our pipeline covers (i) training neural networks for object classification and bounding-box prediction of...

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Veröffentlicht in:	Theory and practice of logic programming Jg. 22; H. 5; S. 739 - 754
Hauptverfasser:	EITER, THOMAS, HIGUERA, NELSON, OETSCH, JOHANNES, PRITZ, MICHAEL
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cambridge, UK Cambridge University Press 01.09.2022
Schlagworte:	Answer set programming Computation theory Datasets Determinism Deterministics Encoding (symbols) Encodings Forecasting Logic programming Natural language processing Network prediction Neural networks neuro-symbolic computation Object classification Original Article Pipelines Program translators Question Answering Questions Statistical analysis Symbolic computation visual question answering neuro-symbolic computation answer-set programming visual question answering
ISSN:	1471-0684, 1475-3081, 1475-3081
Online-Zugang:	Volltext
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