insat3d_imagen
maxim
| insat3d_imagen | maxim | |
|---|---|---|
| 1 | 1 | |
| 9 | 991 | |
| - | - | |
| 3.6 | 0.0 | |
| over 2 years ago | over 1 year ago | |
| Python | Python | |
| GNU General Public License v3.0 only | Apache License 2.0 |
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insat3d_imagen
maxim
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GOOGLE new computer vision multi-axis approach improves high level tasks, such as object detection, as well as motion deblurring, denoising, deraining
Today we present a new multi-axis approach that is simple and effective, improves on the original ViT and MLP models, can better adapt to high-resolution, dense prediction tasks, and can naturally adapt to different input sizes with high flexibility and low complexity. Based on this approach, we have built two backbone models for high-level and low-level vision tasks. We describe the first in “MaxViT: Multi-Axis Vision Transformer”, to be presented in ECCV 2022, and show it significantly improves the state of the art for high-level tasks, such as image classification, object detection, segmentation, quality assessment, and generation. The second, presented in “MAXIM: Multi-Axis MLP for Image Processing” at CVPR 2022, is based on a UNet-like architecture and achieves competitive performance on low-level imaging tasks including denoising, deblurring, dehazing, deraining, and low-light enhancement. To facilitate further research on efficient Transformer and MLP models, we have open-sourced the code and models for both MaxViT and MAXIM.
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