Summary of Interpolating Video-llms: Toward Longer-sequence Lmms in a Training-free Manner, by Yuzhang Shang et al.
Interpolating Video-LLMs: Toward Longer-sequence LMMs in a Training-free Manner
by Yuzhang Shang, Bingxin Xu, Weitai Kang, Mu Cai, Yuheng Li, Zehao Wen, Zhen Dong, Kurt Keutzer, Yong Jae Lee, Yan Yan
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a new approach for integrating video modalities with Large Language Models (LLMs) by introducing an optimizable interface that links sophisticated video encoders to LLMs. This approach, known as Video-LLMs, is typically limited to processing short videos due to computation and data constraints. To address this limitation, the authors propose a method called INTerPolation for Video-LLMs (INTP-Video-LLMs) that allows for the interpolation of video tokens without requiring retraining. The authors introduce an alternative token rearrangement technique that circumvents limitations imposed by the fixed video encoder and alignment projector. Additionally, they propose a training-free LLM context window extension method to enable Video-LLMs to understand longer videos. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding ways to make computers better at understanding long videos using large language models. It’s like trying to teach a computer to watch a movie without getting too confused. The authors have some new ideas on how to do this, and they tested their ideas on a special kind of computer model that can understand short videos. They want to use these ideas to make computers better at understanding longer videos. |
Keywords
» Artificial intelligence » Alignment » Context window » Encoder » Token
