AgentVidBench: A Multi-Hop Video Question Answering Benchmark for Evaluating MLLM Agents

TL;DR

AgentVidBench is a new benchmark designed to evaluate the multi-hop reasoning capabilities of Multimodal Large Language Models (MLLMs) in video question answering. It highlights the limitations of existing models and provides a competitive baseline for future research.

Key Takeaways

• Utilize AgentVidBench to evaluate your MLLM's reasoning skills.
• Incorporate multi-hop reasoning strategies in your video analysis models.
• Experiment with the proposed agentic strategy for improved performance.
• Access the provided datasets to benchmark your models effectively.