PreMind: Multi-Agent Video Understanding for Advanced Indexing of Presentation-style Videos

2025-02-28

AI Summary

To address the challenges of fine-grained information localization and retrieval of visual-domain-specific content (e.g., abbreviations) in online lecture videos, this paper proposes a multi-agent multimodal joint indexing framework. Methodologically, it introduces three novel components: (1) a vision-language model (VLM)-driven speech correction module; (2) a prior-knowledge-enhanced visual understanding module; and (3) a critic-agent-guided iterative visual self-reflection mechanism. The framework integrates VLMs, automatic speech recognition (ASR), multimodal alignment, and multi-agent coordination to achieve semantic video segmentation, cross-modal content extraction, and joint semantic indexing. Evaluated on the LPM benchmark and proprietary enterprise datasets, our approach significantly outperforms existing baselines. Notably, it is the first to enable precise retrieval of terms and abbreviations appearing exclusively in slides—thereby substantially improving indexing granularity and reliability.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

In recent years, online lecture videos have become an increasingly popular resource for acquiring new knowledge. Systems capable of effectively understanding/indexing lecture videos are thus highly desirable, enabling downstream tasks like question answering to help users efficiently locate specific information within videos. This work proposes PreMind, a novel multi-agent multimodal framework that leverages various large models for advanced understanding/indexing of presentation-style videos. PreMind first segments videos into slide-presentation segments using a Vision-Language Model (VLM) to enhance modern shot-detection techniques. Each segment is then analyzed to generate multimodal indexes through three key steps: (1) extracting slide visual content, (2) transcribing speech narratives, and (3) consolidating these visual and speech contents into an integrated understanding. Three innovative mechanisms are also proposed to improve performance: leveraging prior lecture knowledge to refine visual understanding, detecting/correcting speech transcription errors using a VLM, and utilizing a critic agent for dynamic iterative self-reflection in vision analysis. Compared to traditional video indexing methods, PreMind captures rich, reliable multimodal information, allowing users to search for details like abbreviations shown only on slides. Systematic evaluations on the public LPM dataset and an internal enterprise dataset are conducted to validate PreMind's effectiveness, supported by detailed analyses.

Problem

• Develops a system for advanced indexing of lecture videos.
• Enhances video segmentation and multimodal content understanding.
• Improves accuracy in speech transcription and visual content analysis.

Innovation

• Uses Vision-Language Model for video segmentation
• Integrates visual and speech content for indexing
• Employs critic agent for dynamic self-reflection