Tell-and-Answer: Towards Explainable Visual Question Answering using Attributes and Captions (D18-1)
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| Challenge: | Existing approaches to visual question answering represent images using pre-trained CNNs . but they rarely provide any insight, apart from the answer, into the VQA process . |
| Approach: | They propose to break up the end-to-end VQA into two steps: explaining and reasoning . they first extract attributes and generate descriptions as explanations for an image . a reasoning module utilizes these explanations in place of the image to infer an answer . |
| Outcome: | The proposed system achieves comparable performance with baselines, but with added benefits of explanability and the ability to improve with higher quality explanations. |
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| Challenge: | Visual Question Answering (VQA) has benefited from increasingly sophisticated models, but has not enjoyed the same level of engagement in terms of data creation. |
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| Challenge: | Existing benchmarking datasets for visual question answering focus on machine "understanding" but it remains unclear how progress on those datasets corresponds to improvements in this real-world use case. |
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| Challenge: | Visual question answering and image captioning require a shared body of general knowledge connecting language and vision. |
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| Challenge: | Existing approaches to visual question answering (VQA) are not suitable for real-world applications. |
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| Challenge: | Visual Question Answering (VQA) is acknowledged as a challenging multi-modal task for Machine Learning (ML). |
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ProtoVQA: An Adaptable Prototypical Framework for Explainable Fine-Grained Visual Question Answering (2025.emnlp-main)
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| Challenge: | Visual Question Answering (VQA) is increasingly used in diverse applications where models must provide accurate answers and explanations that humans can easily understand and verify. |
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Modular Visual Question Answering via Code Generation (2023.acl-short)
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Sanjay Subramanian, Medhini Narasimhan, Kushal Khangaonkar, Kevin Yang, Arsha Nagrani, Cordelia Schmid, Andy Zeng, Trevor Darrell, Dan Klein
| Challenge: | a framework for visual question answering is based on modular code generation . the scope of reasoning needed for visual questions is vast, and requires many skills . |
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ConceptBert: Concept-Aware Representation for Visual Question Answering (2020.findings-emnlp)
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| Challenge: | Visual Question Answering (VQA) is a challenging task that has received increasing attention from both the computer vision and the natural language processing communities. |
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Towards a Unified Model for Generating Answers and Explanations in Visual Question Answering (2023.findings-eacl)
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| Challenge: | Current explanation generation models are trained to select the best answers from Multiple-Choice questions or to classify single-word answers to a predetermined vocabulary. |
| Approach: | They propose a multitask learning approach towards a Unified Model for Answer and Explanation generation (UMAE) UMAE models surpass the prior state-of-the-art answer accuracy on A-OKVQA by 10 15%, show competitive results on OK-VQA and VCR, and demonstrate promising out-of domain performance on VQA-X. |
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