Summary of Find the Gap: Knowledge Base Reasoning For Visual Question Answering, by Elham J. Barezi et al.
Find The Gap: Knowledge Base Reasoning For Visual Question Answering
by Elham J. Barezi, Parisa Kordjamshidi
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); 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 paper explores knowledge-based visual question answering (KB-VQA), where models must ground questions in visual modalities and retrieve relevant information from a large knowledge base. The authors design neural architectures and train them from scratch, as well as utilize pre-trained language models (LLMs) to analyze the effectiveness of augmenting models with supervised retrieval of external knowledge. Key research questions include whether explicit KB information can improve model performance, how LLMs perform in integrating visual and external knowledge, and whether implicit LLM knowledge can replace explicit KB. The results demonstrate the positive impact of empowering models with supervised external and visual knowledge retrieval, although LLMs excel at 1-hop reasoning but struggle with 2-hop reasoning compared to fine-tuned neural networks (NN) models. Interestingly, LLMs outperform NN models for KB-related questions, highlighting the effectiveness of implicit knowledge in LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks into how computers can answer questions about pictures by using information from a big database. The authors try different ways to make their computer model better at this task. They want to know if giving the model more external information will help it do better, and how well language models that are already good at understanding text will perform when trying to understand images too. The results show that adding more information helps the model do better, but only up to a certain point. The authors also find that these language models are very good at answering simple questions about pictures, but struggle with harder questions. |
Keywords
» Artificial intelligence » Knowledge base » Question answering » Supervised
