How can agents improve video question answering?
VideoMultiAgents: A Multi-Agent Framework for Video Question Answering
This paper introduces VideoMultiAgents, a novel framework for answering questions about videos. It uses multiple specialized AI agents (text, video, and scene-graph analysis) that work independently and report their findings to a central "organizer" agent. This organizer synthesizes the information and selects the best answer. The system also uses a "question-guided" approach to generate captions, focusing on keywords from the question to improve accuracy. Key points for LLM-based multi-agent systems include: the use of specialized agents leveraging different modalities, a central agent for coordinating and synthesizing information, and the benefit of conditioning agent actions (caption generation) on the overall goal (question answering).