@inproceedings{
dong2024internlmxcomposerkhd,
title={Intern{LM}-{XC}omposer2-4{KHD}: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K {HD}},
author={Xiaoyi Dong and Pan Zhang and Yuhang Zang and Yuhang Cao and Bin Wang and Linke Ouyang and Songyang Zhang and Haodong Duan and Wenwei Zhang and Yining Li and Hang Yan and Yang Gao and Zhe Chen and xinyue zhang and Wei Li and Li Jingwen and Wenhai Wang and Kai Chen and Conghui He and Xingcheng ZHANG and Jifeng Dai and Yu Qiao and Dahua Lin and Jiaqi Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=nRp0XhTf61}
}
The Large Vision-Language Model (LVLM) field has seen significant advancements, yet its progression has been hindered by challenges in comprehending fine-grained visual content due to limited resolution. Recent efforts have aimed to enhance the high-resolution understanding capabilities of LVLMs, yet they remain capped at approximately 1500 $\times$ 1500 pixels and constrained to a relatively narrow resolution range. This paper represents InternLM-XComposer2-4KHD, a groundbreaking exploration into elevating LVLM resolution capabilities up to 4K HD (3840 × 1600) and beyond. Concurrently, considering the ultra-high resolution may not be necessary in all scenarios, it supports a wide range of diverse resolutions from 336 pixels to 4K standard, significantly broadening its scope of applicability. Specifically, this research advances the patch division paradigm by introducing a novel extension: dynamic resolution with automatic patch configuration. It maintains the training image aspect ratios while automatically varying patch counts and configuring layouts based on a pre-trained Vision Transformer (ViT) (336 $\times$ 336), leading to dynamic training resolution from 336 pixels to 4K standard. Our research demonstrates that scaling training resolution up to 4K HD leads to consistent performance enhancements without hitting the ceiling of potential improvements. InternLM-XComposer2-4KHD shows superb capability that matches or even surpasses GPT-4V and Gemini Pro in 10 of the 16 benchmarks.
InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD
Xiaoyi Dong Pan Zhang Yuhang Zang Yuhang Cao Bin Wang Linke Ouyang Songyang Zhang Haodong Duan Wenwei Zhang Yining Li Hang Yan Yang Gao Zhe Chen xinyue zhang Wei Li Li Jingwen Wenhai Wang Kai Chen Conghui He Xingcheng ZHANG Jifeng Dai Yu Qiao Dahua Lin Jiaqi Wang