Curriculum Learning for Compositional Visual Reasoning

Wafa Aissa; Wafa Aissa; Marin Ferecatu; Michel Crucianu

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Paper

Curriculum Learning for Compositional Visual Reasoning

Topics: Categorization and Scene Understanding; Deep Learning for Visual Understanding

In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP, 888-897, 2023 , Lisbon, Portugal

Authors: Wafa Aissa ^{1

;

2} ; Marin Ferecatu ¹ and Michel Crucianu ¹

Affiliations: ¹ Cedric Laboratory, Conservatoire National des Arts et Métiers, Paris, France ; ² XXII Group, Paris, France

Keyword(s): Compositional Visual Reasoning, Visual Question Answering, Neural Module Networks, Curriculum Learning.

Abstract: Visual Question Answering (VQA) is a complex task requiring large datasets and expensive training. Neural Module Networks (NMN) first translate the question to a reasoning path, then follow that path to analyze the image and provide an answer. We propose an NMN method that relies on predefined cross-modal embeddings to “warm start” learning on the GQA dataset, then focus on Curriculum Learning (CL) as a way to improve training and make a better use of the data. Several difficulty criteria are employed for defining CL methods. We show that by an appropriate selection of the CL method the cost of training and the amount of training data can be greatly reduced, with a limited impact on the final VQA accuracy. Furthermore, we introduce intermediate losses during training and find that this allows to simplify the CL strategy.

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Paper citation in several formats:

Aissa, W.; Ferecatu, M. and Crucianu, M. (2023). Curriculum Learning for Compositional Visual Reasoning. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP; ISBN 978-989-758-634-7; ISSN 2184-4321, SciTePress, pages 888-897. DOI: 10.5220/0011895400003417

@conference{visapp23,
author={Wafa Aissa. and Marin Ferecatu. and Michel Crucianu.},
title={Curriculum Learning for Compositional Visual Reasoning},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP},
year={2023},
pages={888-897},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011895400003417},
isbn={978-989-758-634-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP
TI - Curriculum Learning for Compositional Visual Reasoning
SN - 978-989-758-634-7
IS - 2184-4321
AU - Aissa, W.
AU - Ferecatu, M.
AU - Crucianu, M.
PY - 2023
SP - 888
EP - 897
DO - 10.5220/0011895400003417
PB - SciTePress