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arXiv · 2505.15928

ViQAgent: Zero-Shot Video Question Answering via Agent with Open-Vocabulary Grounding Validation

Tony Montes·Fernando Lozano
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Abstract

Recent advancements in Video Question Answering (VideoQA) have introduced LLM-based agents, modular frameworks, and procedural solutions, yielding promising results. These systems use dynamic agents and memory-based mechanisms to break down complex tasks and refine answers. However, significant improvements remain in tracking objects for grounding over time and decision-making based on reasoning to better align object references with language model outputs, as newer models get better at both tasks. This work presents an LLM-brained agent for zero-shot Video Question Answering (VideoQA) that combines a Chain-of-Thought framework with grounding reasoning alongside YOLO-World to enhance object tracking and alignment. This approach establishes a new state-of-the-art in VideoQA and Video Understanding, showing enhanced performance on NExT-QA, iVQA, and ActivityNet-QA benchmarks. Our framework also enables cross-checking of grounding timeframes, improving accuracy and providing valuable support for verification and increased output reliability across multiple video domains. The code is available at https://github.com/t-montes/viqagent.

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Tony Montes, Fernando Lozano. 2025-05-21. ViQAgent: Zero-Shot Video Question Answering via Agent with Open-Vocabulary Grounding Validation. https://arxiv.org/abs/2505.15928

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