Summary of Adaptive Patching For High-resolution Image Segmentation with Transformers, by Enzhi Zhang et al.
Adaptive Patching for High-resolution Image Segmentation with Transformers
by Enzhi Zhang, Isaac Lyngaas, Peng Chen, Xiao Wang, Jun Igarashi, Yuankai Huo, Mohamed Wahib, Masaharu Munetomo
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, the authors introduce an innovative approach to improve the efficiency and accuracy of attention-based models in image segmentation tasks. The traditional method of feeding images to transformer encoders involves dividing them into patches and processing each patch sequentially. However, this can be computationally expensive for high-resolution images. To address this challenge, the authors propose a pre-processing technique called Adaptive Mesh Refinement (AMR) that adaptively patches the images based on their details. This reduces the number of patches fed to the model by orders of magnitude, achieving a negligible overhead while maintaining compatibility with any attention-based model. The proposed method demonstrates superior segmentation quality over state-of-the-art models for real-world pathology datasets, with a mean speedup of 6.9 times for resolutions up to 64K^2 on up to 2,048 GPUs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computer vision models work better and faster when looking at very detailed images, like pictures of tiny cells in the body. The current way that models are trained doesn’t work well with these kinds of images because it takes too long and uses too much memory. To solve this problem, the researchers came up with a new method that prepares the images before they’re fed into the model. This makes the process faster and more efficient without changing how the model works. They tested their approach on real-world medical image datasets and found that it produced better results than other models, all while being much faster. |
Keywords
» Artificial intelligence » Attention » Image segmentation » Transformer
