Despite the rapid advancement in the field of image recognition, the
processing of high-resolution imagery remains a computational challenge.
However, this processing is pivotal for extracting detailed object insights in
areas ranging from autonomous vehicle navigation to medical imaging analyses.
Our study introduces a framework aimed at mitigating these challenges by
leveraging memory efficient patch based processing for high resolution images.
It incorporates a global context representation alongside local patch
information, enabling a comprehensive understanding of the image content. In
contrast to traditional training methods which are limited by memory
constraints, our method enables training of ultra high resolution images. We
demonstrate the effectiveness of our method through superior performance on 7
different benchmarks across classification, object detection, and segmentation.
Notably, the proposed method achieves strong performance even on
resource-constrained devices like Jetson Nano. Our code is available at
this https URL

应对高分辨率图像处理中的计算挑战，提出了一种利用内存高效的基于块处理的框架，通过全局上下文表示和局部块信息实现对图像内容的全面理解，并在不受内存限制的情况下对超高分辨率图像进行训练，在分类、目标检测和分割等七个基准测试中取得了卓越性能，甚至在像 Jetson Nano 这样的资源受限设备上也能表现出色。