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  • An improved semantic segmentation algorithm for high . . . - Nature
    The segmentation accuracy is improved, the whole and detailed information of the high-resolution remote sensing image is better identified, and the final model parameter size is 22 96 MB
  • HRSAM: Efficient Interactive Segmentation in High-Resolution . . .
    The Segment Anything Model (SAM) has advanced interactive segmentation but is limited by the high computational cost on high-resolution images This requires downsampling to meet GPU constraints, sacrificing the fine-grained details needed for high-precision interactive segmentation To address SAM's limitations, we focus on visual length extrapolation and propose a lightweight model named
  • Deep learning for efficient high-resolution image processing . . .
    High-resolution images are significant in medical imaging, as their intricate features impact diagnostic accuracy (Krupinski, 2010) Similarly, in satellite imagery, they are essential for applications such as surveillance, urban planning, and digital forensics, where even the smallest details can provide essential evidence (Li, Pei et al , 2020, Yang et al , 2020)
  • High Quality Segmentation for Ultra High-resolution Images
    using low-resolution training images and ultra high-resolution testing images Due to the simple design, even with refining from low to high-resolution, the to-tal inference time is less than half of CascadePSP [9] •In experiments, CRM yields the best segmentation re-sults on ultra high-resolution images It also helps
  • Boost Your Instance Segmentation Accuracy with These Tips
    Image segmentation is a crucial step in various applications, such as photography and object detection To improve instance segmentation accuracy, consider the following tips: Key Takeaways: Choosing the right algorithm is essential for improving instance segmentation accuracy Pre-processing the image before segmentation can lead to better
  • A robust and high-precision edge segmentation and refinement . . .
    To improve the accuracy of high-resolution segmentation, High-Resolution Refine Net (HRRNet) is proposed The network is divided into a rough segmentation module and a refinement module The former improves DeepLabV3+ by adding the shallow features of 1 2 original image size and the corresponding skip connection to obtain better rough
  • Optimizing inference of segmentation on high-resolution . . .
    MLExchange is a machine learning (ML) operations platform providing web user-interfaces (UIs) for data visualization and analysis pipelines at synchrotron facilities Among these UIs is the segmentation app which helps synchrotron users utilize ML algorithms to automatically segment high-resolution scientific images with minimal manual annotation effort In this work, we share code





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