VideoRAG: Retrieval-Augmented Generation over Video Corpus

Retrieval-Augmented Generation (RAG) is a powerful strategy to address the issue of generating factually incorrect outputs in foundation models by retrieving external knowledge relevant to queries and incorporating it into their generation process. However, existing RAG approaches have primarily focused on textual information, with some recent advancements beginning to consider images, and they largely overlook videos, a rich source of multimodal knowledge capable of representing events, processes, and contextual details more effectively than any other modality. While a few recent studies explore the integration of videos in the response generation process, they either predefine query-associated videos without retrieving them according to queries, or convert videos into the textual descriptions without harnessing their multimodal richness. To tackle these, we introduce VideoRAG, a novel framework that not only dynamically retrieves relevant videos based on their relevance with queries but also utilizes both visual and textual information of videos in the output generation. Further, to operationalize this, our method revolves around the recent advance of Large Video Language Models (LVLMs), which enable the direct processing of video content to represent it for retrieval and seamless integration of the retrieved videos jointly with queries. We experimentally validate the effectiveness of VideoRAG, showcasing that it is superior to relevant baselines.

Host: Hey everyone, and welcome back to the podcast! I'm your host, Leo, and I'm super excited for today's episode. We're diving into something really cutting-edge, a topic that's been buzzing in the AI and machine learning world. We'll be exploring a new framework that takes how we use AI to the next level, especially when it comes to videos.

Host: You know, we've seen AI get pretty good at understanding text and even images. But video? That's a whole different ball game. There's so much rich information packed into videos – movement, context, visual cues, spoken words – all happening simultaneously. And that's where this new approach comes in, it's called VideoRAG.

Host: It’s a bit of a mouthful, but the gist is that it’s about making AI better at understanding videos by using something called retrieval-augmented generation. Think of it like giving AI access to a video library, and it's not just passively watching. It can actually pull relevant clips and use that context to answer your questions or generate content. It’s fascinating stuff, and I can’t wait to get into the nitty-gritty.

Host: I've been doing some deep dives into the research, specifically a paper from some really smart folks at KAIST and DeepAuto.ai, and that's what we’re basing our discussion on today. It’s a paper called 'VideoRAG: Retrieval-Augmented Generation over Video Corpus.' It really breaks down the whole process and shows just how much potential there is here.

Host: So, let's kick things off by talking about the core idea behind VideoRAG. We all know about these large language models, or LLMs. They're incredibly powerful, but they sometimes just make stuff up, right? They hallucinate. It's because they're trained on massive datasets, but that knowledge is often incomplete, inaccurate, or outdated. That’s where Retrieval-Augmented Generation, or RAG, comes in. RAG basically gives the model access to external information, so it’s not just relying on what’s already built into its parameters. Instead of just remembering things, it can go look stuff up, much like we would, right?

Host: Exactly, and traditionally, RAG has focused mainly on text – think Wikipedia articles or documents. More recently, people started using images, but, and this is key, video has largely been left out of the equation. Video is a whole different beast, though. It's not just static like text or images. It's got time, movement, actions, sounds. It's a wealth of information that text and images often can’t capture.

Host: So, this paper argues that videos can be a much more effective knowledge source for RAG systems. Think about it – a video can show you how to assemble furniture, demonstrate a science experiment, or capture real-time events. It’s much more engaging and comprehensive than just a text description. The challenge is how to get an AI to understand and use that rich multimodal information effectively. That's where VideoRAG really shines, because it’s not just about retrieving videos, it’s about understanding the content of those videos, both what's happening visually and the text that might accompany it, like subtitles.

Host: This idea is so cool and you can see the massive potential. It's about teaching these AI models to not just see the videos, but to really understand them in all their complexity. So, how does VideoRAG actually work? Let's dive into the method, because it's not just as simple as plugging in a video and going, is it? First, like we talked about, we have to get the videos, and that means having a way to find the right ones that are relevant to the question being asked. It’s like having an intelligent search engine that’s specifically designed for videos.

Host: Yeah, it’s more than just a keyword search though, right? The way they’ve tackled it, from what I understand, involves these large video language models, or LVLMs. These LVLMs are kind of like the brain behind the operation. They've been trained to handle video and text together, processing both visual information and the text associated with a video, like subtitles or captions. These models can take a video and turn it into what they call feature embeddings or essentially visual tokens. They also have a similar process for text, creating text tokens, right? Then, they can analyze all those tokens together.

Host: So, how does it find the relevant videos? The idea is to use these LVLMs to create a representation of the query and each video in the corpus. Then, they use a similarity score – like a cosine similarity – to figure out which videos are most closely related to the query. It's not just about matching keywords, it's about matching the concepts and the meaning of what’s happening in the video with the essence of the question, which is a pretty big step up in sophistication.

Host: It is! And it's that understanding of content, not just keywords, that makes the retrieval process so powerful. The next key part of the process is not just retrieving the relevant videos, it’s about making them useful for generating an answer. I mean, we don't want the AI to just show us the video, we want it to answer our questions based on what it sees. Once the relevant videos are retrieved, they are actually used to create the input to the LVLM, the video and its associated text, then the user’s query are all combined and fed into the model, the model, being an LVLM, has been trained to understand the connections between video, text and the query. The key thing is that it processes all the multimodal information together. It's able to use those video frames, along with text, to generate a response that’s much richer and grounded in actual video content. It's not just paraphrasing or summarizing. It's pulling out specific details and using them to answer the user's questions.

Host: And here's the thing, videos often have textual information, such as subtitles or captions, that's really handy, but many videos don't have this type of text. The creators of VideoRAG anticipated that and came up with a really smart solution to make sure all videos can be useful, regardless of whether they come with text data or not. They use automatic speech recognition or ASR, which is a technology that can take the audio from a video and turn it into text, creating auxiliary text for each video that didn’t already have it. That’s why it’s so powerful because it can use all of the available content.

Host: which is critical for making this system work in the real world. This whole process is a really clever way to combine the power of large language models with the richness of video content. You get the best of both worlds, and that's pretty amazing. So let's dig into how they actually tested this thing. What was the experimental set up they used to validate VideoRAG?

Host: Right, so they didn't just come up with this idea and not test it out, they conducted experiments to see how well it performs against other methods. The setup is quite interesting. They used the WikiHowQA dataset as a source of questions and answers. This dataset has a wide range of instructional questions, perfect for testing RAG systems. Then, they used the HowTo100M dataset as their video library. This dataset is a massive collection of how-to videos from YouTube. Because both these datasets are aligned based on user search results, it works really well for this type of testing.

Host: So, you've got the questions, you've got the videos, and now it's all about putting VideoRAG to the test. They used a few different baselines to see how well VideoRAG compared to other approaches. There was a Naïve method, which is just a baseline, where the AI generates answers without using external information. They also tested two text-based RAG models: TextRAG using BM25 and TextRAG using DPR. These both retrieve documents from Wikipedia. Then they even tested a TextVideoRAG system which used the transcriptions of videos but not the actual video content, to see if it's actually the videos themselves that make the difference.

Host: And then, of course, they tested different versions of their own VideoRAG framework. They had VideoRAG-T, which only used the transcripts from the videos, not the visual content, VideoRAG-V, which only used the video frames and not the transcripts, and finally, VideoRAG-VT, which used both video frames and transcripts. That is so important, to be able to isolate and evaluate which features are actually giving the system its power. They also included a very interesting Oracle setting, which is sort of like the ideal case: they provide the perfectly matched video to the query directly, without going through a retrieval process. This would give them a sense of how much room for improvement there is, if the retrieval process itself could get better, which is pretty clever, right?

Host: Definitely, and to make sure they had a well-rounded evaluation, they used a variety of metrics: ROUGE-L, which looks at the longest common subsequence between the generated answer and the reference answer; BLEU-4, which looks at n-gram overlap; BERTScore, which looks at the semantic alignment between the generated and reference answers using embeddings and finally they used G-Eval, which used an LLM to score the quality of the generated answer. That way they are getting a complete picture of how the system works and how well it performs across different kinds of evaluations.