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A Neuro-Symbolic ASP Pipeline for Visual Question Answering

Published online by Cambridge University Press:  11 July 2022

THOMAS EITER Open the ORCID record for THOMAS EITER [Opens in a new window] ,
NELSON HIGUERA Open the ORCID record for NELSON HIGUERA [Opens in a new window] ,
JOHANNES OETSCH Open the ORCID record for JOHANNES OETSCH [Opens in a new window]  and
MICHAEL PRITZ Open the ORCID record for MICHAEL PRITZ [Opens in a new window]
THOMAS EITER
Affiliation:
Institute of Logic and Computation, Vienna University of Technology (TU Wien), Austria (e-mails: [email protected], [email protected], [email protected], [email protected])
NELSON HIGUERA
Affiliation:
Institute of Logic and Computation, Vienna University of Technology (TU Wien), Austria (e-mails: [email protected], [email protected], [email protected], [email protected])
JOHANNES OETSCH
Affiliation:
Institute of Logic and Computation, Vienna University of Technology (TU Wien), Austria (e-mails: [email protected], [email protected], [email protected], [email protected])
MICHAEL PRITZ
Affiliation:
Institute of Logic and Computation, Vienna University of Technology (TU Wien), Austria (e-mails: [email protected], [email protected], [email protected], [email protected])
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Abstract

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 the CLEVR scenes, (ii) statistical analysis on the distribution of prediction values of the neural networks to determine a threshold for high-confidence predictions, and (iii) a translation of CLEVR questions and network predictions that pass confidence thresholds into logic programmes so that we can compute the answers using an answer-set programming solver. By exploiting choice rules, we consider deterministic and non-deterministic scene encodings. Our experiments show that the non-deterministic scene encoding achieves good results even if the neural networks are trained rather poorly in comparison with the deterministic approach. This is important for building robust VQA systems if network predictions are less-than perfect. Furthermore, we show that restricting non-determinism to reasonable choices allows for more efficient implementations in comparison with related neuro-symbolic approaches without losing much accuracy.

Keywords

answer-set programmingvisual question answeringneuro-symbolic computation
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 22 , Issue 5: 38th International Conference on Logic Programming Special Issue II , September 2022 , pp. 739 - 754
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Copyright
© The Author(s), 2022. Published by Cambridge University Press

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Footnotes

*

This work was partially funded by the Bosch Center for Artificial Intelligence at Renningen, Germany.

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