@inproceedings{
zhu2024parameterinverted,
title={Parameter-Inverted Image Pyramid Networks},
author={Xizhou Zhu and Xue Yang and Zhaokai Wang and Hao Li and Wenhan Dou and Junqi Ge and Lewei Lu and Yu Qiao and Jifeng Dai},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=NKzLqRgG45}
}
Image pyramids are commonly used in modern computer vision tasks to obtain multi-scale features for precise understanding of images. However, image pyramids process multiple resolutions of images using the same large-scale model, which requires significant computational cost. To overcome this issue, we propose a novel network architecture known as the Parameter-Inverted Image Pyramid Networks (PIIP). Our core idea is to use models with different parameter sizes to process different resolution levels of the image pyramid, thereby balancing computational efficiency and performance. Specifically, the input to PIIP is a set of multi-scale images, where higher resolution images are processed by smaller networks. We further propose a feature interaction mechanism to allow features of different resolutions to complement each other and effectively integrate information from different spatial scales. Extensive experiments demonstrate that the PIIP achieves superior performance in tasks such as object detection, segmentation, and image classification, compared to traditional image pyramid methods and single-branch networks, while reducing computational cost. Notably, when applying our method on a large-scale vision foundation model InternViT-6B, we improve its performance by 1\%-2\% on detection and segmentation with only 40\%-60\% of the original computation. These results validate the effectiveness of the PIIP approach and provide a new technical direction for future vision computing tasks.
Parameter-Inverted Image Pyramid Networks
Xizhou Zhu Xue Yang Zhaokai Wang Hao Li Wenhan Dou Junqi Ge Lewei Lu Yu Qiao Jifeng Dai