SiLVR: A Simple Language-based Video Reasoning Framework
Ce Zhang, Yan-Bo Lin, Ziyang Wang, Mohit Bansal, Gedas Bertasius
2025-06-02
Summary
This paper talks about SiLVR, a new system that helps AI models understand and reason about videos more effectively by using language and a smart way to simplify the information they process.
What's the problem?
The problem is that when AI tries to make sense of videos, there is a huge amount of information to handle, which can overwhelm the system and make it hard to focus on the most important parts for answering questions or solving problems.
What's the solution?
The researchers created SiLVR, which uses language to guide the AI and a technique called adaptive token reduction to cut down on unnecessary details, making it easier for the model to pay attention to what really matters in each video.
Why it matters?
This is important because it allows AI to be much better at understanding and explaining what's happening in videos, which can help with things like video search, education, safety monitoring, and making information more accessible.
Abstract
SiLVR, a language-based framework, enhances multimodal LLMs' video reasoning by leveraging adaptive token reduction, achieving top results on several benchmarks.