Computer Science > Computer Vision and Pattern Recognition
arXiv:2311.00308 (cs)
[Submitted on 1 Nov 2023 (v1), last revised 23 Sep 2024 (this version, v2)]
Title:From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities
View a PDF of the paper titled From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities, by Md Farhan Ishmam and 3 other authors
View PDFHTML (experimental)Abstract:The multimodal task of Visual Question Answering (VQA) encompassing elements of Computer Vision (CV) and Natural Language Processing (NLP), aims to generate answers to questions on any visual input. Over time, the scope of VQA has expanded from datasets focusing on an extensive collection of natural images to datasets featuring synthetic images, video, 3D environments, and various other visual inputs. The emergence of large pre-trained networks has shifted the early VQA approaches relying on feature extraction and fusion schemes to vision language pre-training (VLP) techniques. However, there is a lack of comprehensive surveys that encompass both traditional VQA architectures and contemporary VLP-based methods. Furthermore, the VLP challenges in the lens of VQA haven't been thoroughly explored, leaving room for potential open problems to emerge. Our work presents a survey in the domain of VQA that delves into the intricacies of VQA datasets and methods over the field's history, introduces a detailed taxonomy to categorize the facets of VQA, and highlights the recent trends, challenges, and scopes for improvement. We further generalize VQA to multimodal question answering, explore tasks related to VQA, and present a set of open problems for future investigation. The work aims to navigate both beginners and experts by shedding light on the potential avenues of research and expanding the boundaries of the field.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2311.00308 [cs.CV] |
| (orarXiv:2311.00308v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2311.00308 arXiv-issued DOI via DataCite | |
| Related DOI: | https://doi.org/10.1016/j.inffus.2024.102270 DOI(s) linking to related resources |
Submission history
From: Md Farhan Ishmam [view email][v1] Wed, 1 Nov 2023 05:39:41 UTC (1,588 KB)
[v2] Mon, 23 Sep 2024 22:36:53 UTC (37,138 KB)
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View a PDF of the paper titled From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities, by Md Farhan Ishmam and 3 other authors
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