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Expressive Scene Graph Generation Using Commonsense Knowledge Infusion for Visual Understanding and Reasoning

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The Semantic Web (ESWC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13261))

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Abstract

Scene graph generation aims to capture the semantic elements in images by modelling objects and their relationships in a structured manner, which are essential for visual understanding and reasoning tasks including image captioning, visual question answering, multimedia event processing, visual storytelling and image retrieval. The existing scene graph generation approaches provide limited performance and expressiveness for higher-level visual understanding and reasoning. This challenge can be mitigated by leveraging commonsense knowledge, such as related facts and background knowledge, about the semantic elements in scene graphs. In this paper, we propose the infusion of diverse commonsense knowledge about the semantic elements in scene graphs to generate rich and expressive scene graphs using a heterogeneous knowledge source that contains commonsense knowledge consolidated from seven different knowledge bases. The graph embeddings of the object nodes are used to leverage their structural patterns in the knowledge source to compute similarity metrics for graph refinement and enrichment. We performed experimental and comparative analysis on the benchmark Visual Genome dataset, in which the proposed method achieved a higher recall rate (\(R@K = 29.89, 35.4, 39.12\) for \(K = 20, 50, 100\)) as compared to the existing state-of-the-art technique (\(R@K = 25.8, 33.3, 37.8\) for \(K = 20, 50, 100\)). The qualitative results of the proposed method in a downstream task of image generation showed that more realistic images are generated using the commonsense knowledge-based scene graphs. These results depict the effectiveness of commonsense knowledge infusion in improving the performance and expressiveness of scene graph generation for visual understanding and reasoning tasks.

This publication has emanated from research conducted with the financial support of Science Foundation Ireland under Grant number 18/CRT/6223 and 12/RC/2289_P2. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.

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Authors and Affiliations

  1. SFI Centre for Research Training in Artificial Intelligence, Data Science Institute, National University of Ireland, Galway, Galway, Ireland

    Muhammad Jaleed Khan, John G. Breslin & Edward Curry

  2. Insight SFI Research Centre for Data Analytics, Data Science Institute, National University of Ireland, Galway, Galway, Ireland

    John G. Breslin & Edward Curry

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  1. Muhammad Jaleed Khan
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  2. John G. Breslin
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Correspondence to Muhammad Jaleed Khan .

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Editors and Affiliations

  1. University of Amsterdam, Amsterdam, Noord-Holland, The Netherlands

    Paul Groth

  2. Universidad Simón Bolívar, Leibniz Information Centre for Science and Technology, Hannover, Niedersachsen, Germany

    Maria-Esther Vidal

  3. Institut Polytechnique de Paris "DIG", Télécom ParisTech, Palaiseau, France

    Fabian Suchanek

  4. University of Southern California, Marina del Rey, CA, USA

    Pedro Szekley

  5. IBM Research - Thomas J. Watson Research, Yorktown Heights, NY, USA

    Pavan Kapanipathi

  6. LaSIGE, Fac de Ciencias,Edif C6, Pis0 3, Universidade de Lisboa, Lisbon, Portugal

    Catia Pesquita

  7. University of Nantes, Nantes, France

    Hala Skaf-Molli

  8. Aalto University, Espoo, Finland

    Minna Tamper

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Khan, M.J., Breslin, J.G., Curry, E. (2022). Expressive Scene Graph Generation Using Commonsense Knowledge Infusion for Visual Understanding and Reasoning. In: Groth, P., et al. The Semantic Web. ESWC 2022. Lecture Notes in Computer Science, vol 13261. Springer, Cham. https://doi.org/10.1007/978-3-031-06981-9_6

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