Entity-Focused Dense Passage Retrieval for Outside-Knowledge Visual Question Answering (2022.emnlp-main)
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| Challenge: | Existing outsideknowledge visual question answering systems lack retrieved knowledge and supervision is weak . |
| Approach: | They propose an Entity-Focused Retrieval model that provides stronger supervision during training and recognizes question-relevant entities to help retrieve more specific knowledge. |
| Outcome: | The proposed model achieves superior retrieval performance on the currently largest outside-knowledge VQA dataset. |
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| Challenge: | Recent systems that use Dense Passage Retrieval (DPR) for visual question answering use Denses trained separately from answer generation. |
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Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih
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| Challenge: | Open-domain question answering has exploded in popularity due to the success of dense retrieval models. |
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| Challenge: | Recent advances in dense neural retrievers and language models have hindered performance, especially for less common entities and facts. |
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| Challenge: | a framework that leverages the visual-language model to select key knowledge retrieved by DPR and answer questions improves performance of the baseline on the open-domain Knowledge-based VQA benchmark, OK-VQA. |
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| Challenge: | Dense neural text retrieval has achieved promising results on open-domain Question Answering (QA) current dense retrievers require splitting documents into short passages that usually contain local, partial and sometimes biased context, and may yield inaccurate and misleading hidden representations, thus deteriorating the final retrieval result. |
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RG-VQA: Leveraging Retriever-Generator Pipelines for Knowledge Intensive Visual Question Answering (2025.findings-emnlp)
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Settaluri Lakshmi Sravanthi, Pulkit Agarwal, Debjyoti Mondal, Rituraj Singh, Subhadarshi Panda, Ankit Mishra, Kiran Pradeep, Srihari K B, Godawari Sudhakar Rao, Pushpak Bhattacharyya
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