VideoRAG taps into YouTube-like videos to make AI responses more accurate and visually informed.
VideoRAG enhances traditional RAG systems by leveraging video content as external knowledge, using LVLMs to process both visual and textual elements for more accurate and contextually rich responses.
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https://arxiv.org/abs/2501.05874
🤔 Original Problem:
→ Current RAG systems primarily rely on text and sometimes images, missing out on the rich multimodal information available in videos
→ Existing video-based approaches either assume pre-selected videos or convert videos to text, losing valuable visual context
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🔍 Solution in this Paper:
→ VideoRAG dynamically retrieves relevant videos from a large corpus based on query similarity
→ It processes both visual frames and textual elements (subtitles/transcripts) using LVLMs
→ For videos without subtitles, it employs speech recognition to generate auxiliary text
→ The system uses InternVideo2 for video-text alignment during retrieval and LLaVA-Video-7B for response generation
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💡 Key Insights:
→ Combined visual-textual features outperform individual modalities in video retrieval
→ Optimal ratio between textual and visual features is 0.5-0.7 for retrieval performance
→ Visual information is crucial for queries requiring demonstration or temporal understanding
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📊 Results:
→ Outperforms text-based RAG baselines across ROUGE-L (0.254 vs 0.172), BLEU-4 (0.054 vs 0.032)
→ Shows 25% improvement in retrieval accuracy when combining visual and textual features
→ Particularly excels in Food & Entertainment category due to strong visual components
