Retrieval-Based Video Language Model for Efficient Long Video Question Answering

In this article, we explore how to improve the efficiency and accuracy of video question answering systems by leveraging powerful language models (LLMs) while addressing the challenges of long videos. Our proposed framework incorporates a novel question-guided retrieval mechanism that identifies relevant video chunks and selects only a few visual tokens as context for the LLM inference, reducing computational costs and noise interference.
To better understand the challenges of long videos, we draw inspiration from the biological concept of working memory, which selectively retrieves and manipulates information necessary for complex tasks. Our approach is designed to mimic this process by identifying and concentrating on relevant video segments while filtering out irrelevant data.
To achieve this, we introduce a question-guided retrieval mechanism that uses the input question to identify the most relevant video chunks and their associated visual tokens as context for the LLM inference. This reduces the number of video tokens, preserves the most informative information, and eliminates noise interference, resulting in enhanced system performance.
Our experimental results demonstrate the effectiveness of our designs for comprehending long videos, providing a more efficient and accurate way to answer questions based on visual content. By leveraging powerful LLMs and incorporating a question-guided retrieval mechanism, we have created a framework that can efficiently and accurately understand long videos, making it easier for users to find the information they need.
In summary, our proposed framework is designed to overcome the challenges of long videos by using a novel question-guided retrieval mechanism that identifies relevant video chunks and reduces computational costs and noise interference. Our approach improves the efficiency and accuracy of video question answering systems, making it easier for users to find the information they need.