arXiv:2605.31069v1 Announce Type: cross Abstract: Accurately predicting future events is fundamental to content understanding and decision-making across various domains. While prior research has primarily focused on text or short-video scenarios, long-video event prediction, characterized by vast multimodal context and more complex narratives, remains underexplored. Meanwhile, although recent Long-Video Language Models (LVLMs), built on Large Language Models (LLMs) and Vision-Language Models (VLMs), have shown promise in long-video question answering and summarization, they struggle to general
The proliferation of long-form video content and the rapid advancements in large language models and vision-language models are creating an urgent need for more sophisticated long-video understanding and prediction capabilities.
Improving event prediction in long videos is crucial for AI's ability to interpret complex real-world scenarios, enhance human-computer interaction, and automate decision-making across critical applications.
This research outlines a methodology to overcome current limitations of LVLMs in handling long-video event prediction, potentially leading to more accurate and reliable AI systems for understanding extended narratives.
- · AI researchers
- · Video platforms
- · Security and surveillance
- · Content analysis
- · AI models without advanced long-video understanding
- · Manual video analysis tools
AI systems will gain improved capabilities in understanding and predicting events within extended video sequences.
This will enable more sophisticated automated monitoring, content generation, and decision support in environments rich with long-form video data.
The enhanced AI understanding of long-term temporal dynamics could accelerate the development of more capable and autonomous AI agents in complex, unstructured environments.
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Read at arXiv cs.CL