Summary of Multimodal Reranking For Knowledge-intensive Visual Question Answering, by Haoyang Wen et al.
Multimodal Reranking for Knowledge-Intensive Visual Question Answering
by Haoyang Wen, Honglei Zhuang, Hamed Zamani, Alexander Hauptmann, Michael Bendersky
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the challenge of incorporating external knowledge into visual question answering models, which requires effective retrieval and ranking of relevant information. The authors propose an additional module, a multi-modal reranker, to improve the quality of candidate selection for answer generation. This module leverages cross-item interactions between candidates and questions to model relevance scores more accurately. Experimental results on OK-VQA and A-OKVQA datasets demonstrate consistent improvements when using this approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make computers better at understanding images and answering questions about what’s in them. Right now, these machines struggle to use the right information from the internet to answer questions. The authors suggest a new way of organizing and using this information that makes it easier for the computer to find the correct answers. They test their idea on two big datasets and show that it works better than before. |
Keywords
» Artificial intelligence » Multi modal » Question answering
