Summary of Modality-aware Integration with Large Language Models For Knowledge-based Visual Question Answering, by Junnan Dong et al.
Modality-Aware Integration with Large Language Models for Knowledge-based Visual Question Answering
by Junnan Dong, Qinggang Zhang, Huachi Zhou, Daochen Zha, Pai Zheng, Xiao Huang
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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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 The proposed novel modality-aware integration with large language models (LLMs) for knowledge-based visual question answering (KVQA) tackles the challenges of hallucinations and multimodal alignment in complex scenarios. The method, dubbed MAIL, leverages multimodal knowledge for image understanding and knowledge reasoning through a two-stage prompting strategy with LLMs, constructing a coupled concept graph, and designing a pseudo-siamese graph medium fusion. This approach carefully integrates visual features from images, entities from KGs, and concepts from LLMs to answer complex visual questions. The results show the superiority of MAIL on two benchmark datasets with 24x less resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to answer a question about an image by using information from multiple sources like a dictionary, a book, or the internet. This is called Knowledge-Based Visual Question Answering (KVQA). It’s hard because these sources might not agree on what’s in the picture or what it means. The solution proposed in this paper, called MAIL, uses special computer programs to help match up information from different sources and make sure they’re all working together correctly. This makes it easier to answer questions about complex images. |
Keywords
* Artificial intelligence * Alignment * Prompting * Question answering
