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A cloud removal dataset and a diffusion-based cloud removal method
Crop type mapping of small holder farms in Ghana, and South Sudan
An implementation of 'simple diffusion: End-to-end diffusion for high resolution images' as published by Hoogeboom et al.
A PyTorch implementation of the paper "All are Worth Words: A ViT Backbone for Diffusion Models".
[NeurIPS 2024] The official code of "U-DiTs: Downsample Tokens in U-Shaped Diffusion Transformers"
[Pattern Recognition Letters] This is the official code of the paper "Cloud removal using SAR and optical images via attention mechanism-based GAN"
标注自己的数据集，训练、评估、测试、部署自己的人工智能算法
📃 开箱即用的 Markdown 简历，支持 VSCode / Obsidian / Typora
Implementation of Machine Learning and Deep Learning techniques to find insights from the satellite data.
[TGRS 2024] DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal from Optical Satellite Images
Medical Image Segmentation Method Based on Swin Transformer with Diffusion Probabilistic Model
Text Diffusion Model with Encoder-Decoder Transformers for Sequence-to-Sequence Generation [NAACL 2024]
DSen2-CR: A network for removing clouds from Sentinel-2 images. This repo contains the model code, written in Python/Keras, as well as links to pre-trained checkpoints and the SEN12MS-CR dataset.
Official PyTorch Implementation of "Scalable Diffusion Models with Transformers"
This may be the simplest implement of DDPM. You can directly run Main.py to train the UNet on CIFAR-10 dataset and see the amazing process of denoising.
Implementations of recent research prototypes/demonstrations using MONAI.
A list of 3D computer vision papers with Transformers
Q. Zhang, Q. Yuan, J. Li, Z. Li, H. Shen, and L. Zhang, "Thick Cloud and Cloud Shadow Removal in Multitemporal Images using Progressively Spatio-Temporal Patch Group Learning", ISPRS Journal, 2020.
Collection of popular and reproducible works of cloud detection and removal.
Video Swin Transformer - PyTorch
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch