arXiv:2605.21917v2 Announce Type: replace-cross Abstract: Training Vision Language Models (VLMs) for video event reasoning requires high-quality structured annotations capturing not only what happened, but when, where, why, and with what consequence, at a scale manual labelling cannot support. We present MAVEN (Multi-stage Agentic Video Event aNnotation), a multi-stage agentic pipeline that turns raw videos into multi-task training data with Chain-of-Thought (CoT) reasoning traces, organized around a designated Event of Focus. At its core, MAVEN synthesizes a Multi-Scale Spatio-Temporal Event
The increasing complexity of video reasoning tasks for AI and the limitations of manual annotation are driving the need for automated, scalable solutions.
This development addresses a critical bottleneck in training advanced Vision Language Models, impacting the scalability and sophistication of AI applications that require understanding dynamic real-world events.
The ability to generate high-quality, structured video annotations automatically with Chain-of-Thought reasoning traces will accelerate VLM development and deployment for complex tasks.
- · AI model developers
- · Video analytics companies
- · Robotics and automation sectors
- · Generative AI platforms
- · Manual annotation services
- · Companies reliant on limited, simple video datasets
Automated data generation will significantly reduce the cost and time associated with training advanced video understanding AI models.
More sophisticated video reasoning capabilities will enable new applications in areas like intelligent surveillance, autonomous systems, and content generation.
The enhanced ability of AIs to interpret human actions and intentions from video could lead to more nuanced human-robot interaction and more effective AI agents in dynamic environments.
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Read at arXiv cs.AI