ProMSA:Progressive Multimodal Search Agents for Knowledge-Based Visual Question Answering
Learn how ProMSA, a progressive multimodal search agent, improves Knowledge-Based Visual Question Answering by adaptively combining image and text search, and why this matters for AI models that need to reason with external knowledge
- Build a ProMSA model using the arXiv paper as a reference
- Run experiments to evaluate the performance of ProMSA on KB-VQA tasks
- Configure the model to adaptively choose between image and text search
- Test the model on various datasets to assess its generalizability
- Apply ProMSA to real-world applications such as visual question answering and multimodal search
AI engineers and researchers on a team can benefit from ProMSA as it enhances the capabilities of KB-VQA models, while data scientists can leverage this technology to improve multimodal search and retrieval systems
💡 ProMSA's adaptive search strategy improves KB-VQA performance by combining image and text search in a progressive manner
🤖 ProMSA: a progressive multimodal search agent for KB-VQA! 📸📚
Key Takeaways
Learn how ProMSA, a progressive multimodal search agent, improves Knowledge-Based Visual Question Answering by adaptively combining image and text search, and why this matters for AI models that need to reason with external knowledge
DeepCamp AI