Meet VideoRAG: A Retrieval-Augmented Generation (RAG) Framework Leveraging Video Contents for Enveloped Query Responses

Video-based technologies have become important tools for acquiring information and understanding complex concepts. Videos include visual, temporal, and contextual data, providing a more diverse representation than still images and text. With the growing popularity of video-sharing platforms and the vast repositories of educational and informational videos available on the Internet, effective videos as sources of information offer unprecedented opportunities to answer questions that require detailed context, spatial understanding, and representational process.

Augmented-production systems, which include retrieval and feedback processing, often ignore the full potential of video data. These systems usually rely on textual information or occasionally include still images to support answers to questions. However, they fail to capture the richness of videos, which include visual dynamics and multimodal cues that are important for complex tasks. Conventional methods describe the videos related to the question without retrieving or convert the videos into text formats, losing important information such as visual context and temporal dynamics. This inadequacy interferes with providing accurate and informative answers to real-world, multi-method questions.

Current methods have explored text or image-based detection but have not yet fully exploited video data. In conventional RAG systems, video content is presented as subtitles or captions, focusing only on textual features or reduced to preselected frames for target analysis. Both methods limit the multimodal richness of videos. In addition, the lack of dynamic retrieval techniques and query-related video integration limits the effectiveness of these systems. The lack of complete video integration leaves an untapped opportunity to develop a paradigm for extended retrieval generation.

Research teams from KaiST and DeepAuto.ai have proposed a novel framework called VideoRAG to address the challenges associated with using video data in retrieval-augmented production systems. VideoRAG dynamically retrieves query-related videos from a large corpus and integrates visual and textual information into the production process. It leverages the power of Large Video Language Models (LVLMs) for seamless integration of multimodal data. This method represents a significant improvement over previous methods by ensuring that the returned videos are relevant to user queries and maintaining the temporal richness of the video content.

The proposed methodology involves two main stages: retrieval and production. It then identifies videos by their similar visual and textual features about the query during retrieval. VideoRAG uses automatic speech recognition to generate textual data for video that is not available with subtitles. This section ensures that the generation of responses to all videos receives valuable contributions from each video. The relevant returned videos are also fed into the framework's production module, where multimodal data such as frames, subtitles, and query text are combined. This input is fully processed in LVLMs, enabling them to produce long, rich, accurate, and contextually appropriate responses. VideoRAG's focus on visual and textual integration makes it possible to represent complex objects in complex processes and interactions that cannot be explained using static methods.

VideoRAG was extensively tested on datasets such as WikiHowQA and HowTo100M. These datasets include a wide variety of questions and video content. In particular, the method reveals a better response quality, according to various metrics, such as ROUGE-L, BLEU-4, and BERTScore. Therefore, for the VideoRAG method, the score was 0.254 according to ROUGE-L, while for text-based methods, RAG reported 0.228 as the highest score. The same is shown for BLEU-4, n-gram overlap: for VideoRAG; this is 0.054; based on the text, it was only 0.044. The framework variant, which used both video and text frames, further improved performance, reaching a BERTScore of 0.881, compared to 0.870 for the baseline methods. These results highlight the importance of multimodal integration in improving response accuracy and underscore the transformative potential of VideoRAG.

The authors demonstrated that VideoRAG's ability to combine visual and written data leads to richer and more accurate responses. Compared to traditional RAG systems that rely only on textual or still image data, VideoRAG is more effective in situations that require detailed spatial and temporal understanding. Including text-assisted production of videos without subtitles ensures consistent performance across various datasets. By enabling retrieval and processing based on the video corpus, the framework addresses the limitations of existing methods and sets a benchmark for future multimodal-augmented retrieval systems.

In short, VideoRAG represents a major step forward in advanced retrieval production systems because it uses video content to improve response quality. This model combines advanced retrieval techniques with the power of LVLMs to deliver rich, accurate responses. Functionally, it effectively addresses the shortcomings of current systems, thereby providing a robust framework for incorporating video data into information production pipelines. With its high performance over various metrics and datasets, VideoRAG proves itself as a new way to incorporate videos into extended production systems for retrieval.

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Nikhil is an intern consultant at Marktechpost. He is pursuing a dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is constantly researching applications in fields such as biomaterials and biomedical sciences. With a strong background in Material Science, he explores new developments and creates opportunities to contribute.