A Survey of Available Techniques for Naive Artificial Intelligence based System for Conversing with a Human

Rayyan Hashmi and Ayan Rajput

International Journal of Scientific Research in Science and Technology, 2024, vol. 11, issue 3, 865-876

Abstract: Visual questions selectively target different areas of an image, including background details and underlying context. As a result, a system that succeeds at VQA typically needs a more detailed understanding of the image and complex reasoning than a system producing generic image captions. Moreover, VQA is amenable to automatic evaluation, since many open-ended answers contain only a few words or a closed set of answers that can be provided in a multiple-choice format. We provide a dataset containing ∼0.25M images, ∼0.76M questions, and ∼10M answers (www.visualqa.org), and discuss the information it provides. Numerous baselines and methods for VQA are provided and compared with human performance. ”

Keywords: Visual Question Answering; visualqa; AI; Natural Language Processing (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v11:y2024:i3:id:272

DOI: 10.32628/IJSRST2411362

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