  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. Our code and models are available
at https://github.com/OpenGVLab/PIIP.
