@inproceedings{
wu2024videollmmod,
title={Video{LLM}-MoD: Efficient Video-Language Streaming with Mixture-of-Depths Vision Computation},
author={Shiwei Wu and Joya Chen and Kevin Qinghong Lin and Qimeng Wang and Yan Gao and Qianli Xu and Tong Xu and Yao Hu and Enhong Chen and Mike Zheng Shou},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=NKPXHzYusG}
}
A well-known dilemma in large vision-language models (e.g., GPT-4, LLaVA) is that while increasing the number of vision tokens generally enhances visual understanding, it also significantly raises memory and computational costs, especially in long-term, dense video frame streaming scenarios. Although learnable approaches like Q-Former and Perceiver Resampler have been developed to reduce the vision token burden, they overlook the context causally modeled by LLMs (i.e., key-value cache), potentially leading to missed visual cues when addressing user queries. In this paper, we introduce a novel approach to reduce vision compute by leveraging redundant vision tokens ``skipping layers'' rather than decreasing the number of vision tokens. Our method, VideoLLM-MoD, is inspired by mixture-of-depths LLMs and addresses the challenge of numerous vision tokens in long-term or streaming video. Specifically, for certain transformer layer, we learn to skip the computation for a high proportion (e.g., 80\%) of vision tokens, passing them directly to the next layer. This approach significantly enhances model efficiency, achieving approximately 42% time and 30% memory savings for the entire training. Moreover, our method reduces the computation in the context and avoid decreasing the vision tokens, thus preserving or even improving performance compared to the vanilla model. We conduct extensive experiments to demonstrate the effectiveness of VideoLLM-MoD, showing its state-of-the-art results on multiple benchmarks, including narration, forecasting, and summarization tasks in COIN, Ego4D, and Ego-Exo4D datasets. The code and checkpoints will be made available at github.com/showlab/VideoLLM-online.
VideoLLM-MoD: Efficient Video-Language Streaming with Mixture-of-Depths Vision Computation
Shiwei Wu Joya Chen Kevin Qinghong Lin Qimeng Wang Yan Gao Qianli Xu Tong Xu Yao Hu Enhong Chen Mike Zheng Shou