Parameter-Inverted Image Pyramid Networks for Visual Perception and
  Multimodal Understanding
Authors: name: Zhaokai Wang, name: Xizhou Zhu, name: Xue Yang, name: Gen Luo, name: Hao Li, name: Changyao Tian, name: Wenhan Dou, name: Junqi Ge, name: Lewei Lu, name: Yu Qiao, name: Jifeng Dai

Image pyramids are widely adopted in top-performing methods to obtain
multi-scale features for precise visual perception and understanding. However,
current image pyramids use the same large-scale model to process multiple
resolutions of images, leading to significant computational cost. To address
this challenge, we propose a novel network architecture, called
Parameter-Inverted Image Pyramid Networks (PIIP). Specifically, PIIP uses
pretrained models (ViTs or CNNs) as branches to process multi-scale images,
where images of higher resolutions are processed by smaller network branches to
balance computational cost and performance. To integrate information from
different spatial scales, we further propose a novel cross-branch feature
interaction mechanism. To validate PIIP, we apply it to various perception
models and a representative multimodal large language model called LLaVA, and
conduct extensive experiments on various tasks such as object detection,
segmentation, image classification and multimodal understanding. PIIP achieves
superior performance compared to single-branch and existing multi-resolution
approaches with lower computational cost. When applied to InternViT-6B, a
large-scale vision foundation model, PIIP can improve its performance by 1%-2%
on detection and segmentation with only 40%-60% of the original computation,
finally achieving 60.0 box AP on MS COCO and 59.7 mIoU on ADE20K. For
multimodal understanding, our PIIP-LLaVA achieves 73.0% accuracy on TextVQA and
74.5% on MMBench with only 2.8M training data. Our code is released at
https://github.com/OpenGVLab/PIIP.