Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Cross-domain Correspondence Learning for Exemplar-based Image Translation. (CVPR 2020 Oral)
| Date | Stars |
|---|---|
| 2026-07-24 | 399 |
| 2026-07-25 | 399 |
| 2026-07-28 | 399 |
| 2026-07-30 | 399 |
| 2026-08-21 | 399 |
| 2026-09-17 | 400 |
| 2026-09-20 | 400 |
Today
— stars today
This week
+1 stars this week
This month
+1 stars this month
Momentum
0.0
growth rate 0.25%/day
# Cross-domain Correspondence Learning for Exemplar-based Image Translation (CVPR 2020 oral, official Pytorch implementation)  ### [Project page](https://panzhang0212.github.io/CoCosNet/) | [Paper](https://arxiv.org/abs/2004.05571) | [Video](https://www.youtube.com/watch?v=BdopAApRSgo&feature=emb_logo) <!-- Cross-domain Correspondence Learning for Exemplar-based Image Translation (CVPR 2020 Oral). --> <!-- <br> --> [Pan Zhang](https://panzhang0212.github.io/), [Bo Zhang](https://www.microsoft.com/en-us/research/people/zhanbo/), [Dong Chen](https://www.microsoft.com/en-us/research/people/doch/), [Lu Yuan](https://www.microsoft.com/en-us/research/people/luyuan/), and [Fang Wen](https://www.microsoft.com/en-us/research/people/fangwen/). <!-- <br> --> ## Abstract >We present a general framework for exemplar-based image translation, which synthesizes a photo-realistic image from the input in a distinct domain (e.g., semantic segmentation mask, or edge map, or pose keypoints), given an exemplar image. The output has the style (e.g., color, texture) in consistency with the semantically corresponding objects in the exemplar. We propose to jointly learn the cross domain correspondence and the image translation, where both tasks facilitate each other and thus can be learned with weak supervision. The images from distinct domains are first aligned to an intermediate domain where dense correspondence is established. Then, the network synthesizes images based on the appearance of semantically corresponding patches in the exemplar. We demonstrate the effectiveness of our approach in several image translation tasks. Our method is superior to state-of-the-art methods in terms of image quality significantly, with the image style faithful to the exemplar with semantic consistency. Moreover, we show the utility of our method for several applications. ## :sparkles: News 2022.12 We propose [Paint by Example](https://github.com/Fantasy-Studio/Paint-by-Example) which allows in the wild image editing according to an examplar based on **stable diffusion**. One can have a try for our [online demo](https://huggingface.co/spaces/Fantasy-Studio/Paint-by-Example). 2022.8 We recently propose [PITI](https://github.com/PITI-Synthesis/PITI) which is a SOTA image-to-image translation method based on *prtrained diffusion model*. 2021.5 We recently propose [CoCosNet v2](https://github.com/microsoft/CoCosNet-v2), which brings more stunning results for high-resolution images. Welcome to have a try. ## Demo <!--  --> <p align="center"> <img src="imgs/demo.gif"> </p> ## Installation Clone the Synchronized-BatchNorm-PyTorch repository. ``` cd models/networks/ git clone https://github.com/vacancy/Synchronized-BatchNorm-PyTorch cp -rf Synchronized-BatchNorm-PyTorch/sync_batchnorm . cd ../../ ```` Install dependencies: ```bash pip install -r requirements.txt ```` ## Inference Using Pretrained Model #### 1) ADE20k (mask-to-image) Download the pretrained model from [here](https://drive.google.com/drive/folders/1BEBBENbEr9tutZsyGGc3REUuuOYqf6M3?usp=sharing) and save them in `checkpoints/ade20k`. Then run the command ````bash python test.py --name ade20k --dataset_mode ade20k --dataroot ./imgs/ade20k --gpu_ids 0 --nThreads 0 --batchSize 6 --use_attention --maskmix --warp_mask_losstype direct --PONO --PONO_C ```` The results are saved in [`output/test/ade20k`](https://github.com/panzhang0212/CoCosNet_Pytorch/blob/master/output/test/ade20k/0.png). If you don't want to use mask of exemplar image when testing, you can download model from [here](https://drive.google.com/drive/folders/1m4LXbOc00cu8hXCgf-_N55AIAE9R__m6?usp=sharing), save them in `checkpoints/ade20k`, and run ```` bash python test.py --name ade20k --dataset_mode ade20k --dataroot ./imgs/ade20k --gpu_ids 0 --nThreads 0 --batchSize 6 --use_attention --maskmix --noise_for_mask --warp_mask_losstype direct --PONO --PONO_C --which_epoch 90 ```` #### 2) Cel
Excerpt of 12,550 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:4eb82b6d6c7ed631, topic:deep-learning, topic:pytorch
matched fp:4eb82b6d6c7ed631, topic:computer-vision, readme:semantic segmentation