Vol. 2 No. 2 (2022): Journal of AI-Assisted Scientific Discovery
Articles

Visual Question Answering - Challenges and Solutions: Studying challenges and solutions in visual question answering (VQA) systems for understanding and answering questions about images

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  • Dr. Carlos Hernández
Dr. Carlos Hernández
Associate Professor of Information Technology, National Autonomous University of Mexico (UNAM)
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Published 19-09-2022

Keywords

  • VQA,
  • Attention Mechanisms,
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Carlos Hernández, “Visual Question Answering - Challenges and Solutions: Studying challenges and solutions in visual question answering (VQA) systems for understanding and answering questions about images”, Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, pp. 88–97, Sep. 2022, Accessed: Nov. 26, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/117

Abstract

Visual Question Answering (VQA) is a challenging task that requires machines to comprehend images and answer natural language questions about them. This paper presents an overview of the challenges faced by VQA systems and explores the solutions proposed to address these challenges. We discuss the complexities of multimodal understanding, the need for common-sense reasoning, and the importance of interpretability in VQA. We also highlight the role of large-scale datasets and benchmarking in advancing the field. Additionally, we examine recent trends such as attention mechanisms, graph-based reasoning, and pre-trained models in improving VQA performance. Through this paper, we aim to provide insights into the current state of VQA research and directions for future work.

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References

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