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Retrieval-Augmented Generation in Video: Insights into VideoRAG

The rapid development in the field of Artificial Intelligence (AI) has led to impressive advancements in large language models (LLMs). These models can generate text, answer questions, and even create creative content. A known problem with these models, however, is the generation of factually incorrect information, so-called hallucinations. Retrieval-Augmented Generation (RAG) has proven to be a promising strategy to address this problem. It retrieves relevant information from external sources and integrates it into the generation process. Until now, RAG approaches have mainly focused on text data, with some recent developments also incorporating images. However, videos, as a rich source of multimodal knowledge, have been largely overlooked.

VideoRAG closes this gap by extending the benefits of RAG to the video domain. Instead of being limited to predefined videos or converting videos into pure text descriptions, VideoRAG leverages the multimodal richness of videos. The framework dynamically retrieves relevant videos based on their relevance to the given queries and uses both visual and textual information from the videos for generating answers.

How VideoRAG Works

VideoRAG is based on large video-language models (LVLMs). These models enable the direct processing of video content to represent it for retrieval and seamlessly integrate the retrieved videos with the queries. The process can be divided into three phases:

1. Query Decomposition: The LVLM analyzes the query and generates a search query for relevant video segments.

2. Video Text Generation and Retrieval: The video is processed in parallel to extract different types of text information, including optical character recognition (OCR), automatic speech recognition (ASR), and object recognition. Relevant text excerpts are retrieved based on the search query.

3. Integration and Generation: The retrieved text excerpts are combined with the original query and the visual information from the video to generate a comprehensive and well-founded answer.

Advantages of VideoRAG

VideoRAG offers several advantages over conventional approaches:

Dynamic Retrieval: Videos are retrieved dynamically based on the query, rather than relying on predefined videos. This allows for more precise and context-specific information retrieval.

Multimodal Integration: VideoRAG utilizes both visual and textual information from videos to enable a more comprehensive understanding of the content.

Seamless Integration with LVLMs: The use of LVLMs allows for direct processing of video content and efficient integration of the retrieved information into the generation process.

Applications of VideoRAG

The application possibilities of VideoRAG are diverse and range from answering questions about videos to creating summaries and generating new video content. Potential fields of application include:

Education: VideoRAG can be used to create learning materials and answer questions about educational videos.

Customer Service: VideoRAG can be integrated into chatbots to answer customer inquiries with relevant video information.

Content Creation: VideoRAG can support the creation of video summaries and the generation of new video content based on existing videos.

Future Developments

VideoRAG is still in its early stages of development. Future research could focus on improving the accuracy and efficiency of retrieval and generation. Further research directions include the integration of additional modalities such as audio and the development of more robust evaluation metrics for VideoRAG systems.

With the further development of LVLMs and the increasing availability of large, annotated video databases, VideoRAG has the potential to fundamentally change the way we interact with and utilize video content. It opens up new possibilities for information retrieval, content creation, and the development of intelligent video applications.

Bibliographie: Jeong, S., Kim, K., Baek, J., & Hwang, S. J. (2025). VideoRAG: Retrieval-Augmented Generation over Video Corpus. arXiv preprint arXiv:2501.05874. Tevissen, Y., Guetari, K., & Petitpont, F. (2024). Towards Retrieval Augmented Generation over Large Video Libraries. arXiv preprint arXiv:2406.14938. Luo, Y., Zheng, X., Yang, X., Li, G., Lin, H., Huang, J., ... & Ji, R. (2024). Video-RAG: Visually-aligned Retrieval-Augmented Long Video Comprehension. arXiv preprint arXiv:2411.13093. Dela Rosa, K. (2024). Video Enriched Retrieval Augmented Generation Using Aligned Video Captions. arXiv preprint arXiv:2405.17706. Arefeen, M. S. I., Hoque, A. S. M. L., & Rahman, M. M. (2024). ViTA: An Efficient Video-to-Text Algorithm using VLM for RAG-based Question Answering. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 1172-1179. Ruvini, J. D. (2024). LLM Papers Reading Notes - December 2024. LinkedIn. Chandak, N., Bantilan, N., Bhattacharya, A., ... & Bowman, S. R. (2024). Towards Interactive Long-Form Video Understanding with Multimodal Language Models. arXiv preprint arXiv:2412.04085. ```