Update README.md

README.md

@@ -1,14 +1,29 @@
 # Mnist
-The Pytorch Implementation of "Appearance-Based Gaze Estimation in the Wild". (updated in 2021/04/25)
+The Pytorch Implementation of "Appearance-Based Gaze Estimation in the Wild". (updated in 2021/04/28)
 
-This is the implemented version metioned in our survey **"Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark"**.
-Please refer our paper or visit our benchmark website <a href="http://phi-ai.org/GazeHub/" target="_blank">*GazeHub*</a> for more information.
-The performance of this version is reported in the website.
 
-To know more detail about the method, please refer the origin paper.
+We build benchmarks for gaze estimation in our survey [**"Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark"**](https://arxiv.org/abs/2104.12668).
+This is the implemented code of "Full-face" methods in our benchmark. Please refer our survey for more details.
 
-We recommend you to use the data processing code provided in <a href="http://phi-ai.org/GazeHub/" target="_blank">*GazeHub*</a>.
-You can use the processed dataset and this code for directly running.
+We recommend you to use **data processing codes** provided in <a href="http://phi-ai.org/GazeHub/" target="_blank">*GazeHub*</a>.
+You can direct run the method' code using the processed dataset.
+
+## Links to gaze estimation codes.
+
+- A Coarse-to-fine Adaptive Network for Appearance-based Gaze Estimation, AAAI 2020 (Coming soon)
+- [Gaze360: Physically Unconstrained Gaze Estimation in the Wild](https://github.com/yihuacheng/Gaze360), ICCV 2019
+- [Appearance-Based Gaze Estimation Using Dilated-Convolutions](https://github.com/yihuacheng/Dilated-Net), ACCV 2019
+- [Appearance-Based Gaze Estimation via Evaluation-Guided Asymmetric Regression](https://github.com/yihuacheng/ARE-GazeEstimation), ECCV 2018
+- [RT-GENE: Real-Time Eye Gaze Estimation in Natural Environments](https://github.com/yihuacheng/RT-Gene), ECCV 2018
+- [MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation](https://github.com/yihuacheng/Gaze-Net), TPAMI 2017
+- [It’s written all over your face: Full-face appearance-based gaze estimation](https://github.com/yihuacheng/Full-face), CVPRW 2017
+- [Eye Tracking for Everyone](https://github.com/yihuacheng/Itracker), CVPR 2016
+- [Appearance-Based Gaze Estimation in the Wild](https://github.com/yihuacheng/Mnist), CVPR 2015
+
+## Performance
+The method is evaluated in three tasks. Please refer our survey for more details.
+![benchmarks](benchmarkA.png)
+![benchmarks](benchmarkB.png)
 
 ## License
 The code is under the license of [CC BY-NC-SA 4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/).
@@ -27,7 +42,7 @@ The project contains following files/folders.
 
 Normally, for training, you should change 
 1. `train.save.save_path`, The model is saved in the `$save_path$/checkpoint/`.
-2. `train.data.image`, This is the path of image.
+2. `train.data.image`, This is the path of image,  please use the provided data processing code in <a href="http://phi-ai.org/GazeHub/" target="_blank">*GazeHub*</a>
 3. `train.data.label`, This is the path of label.
 4. `reader`, This indicates the used reader. It is the filename in `reader` folder, e.g., *reader/reader_mpii.py* ==> `reader: reader_mpii`.
 
@@ -42,7 +57,7 @@ You can run
 ```
 python train.py config/config_mpii.yaml 0
 ```
-This means the code running with `config_mpii.yaml` and use the `0th` person as the test set.
+This means the code will run with `config_mpii.yaml` and use the `0th` person as the test set.
 
 You also can run
 ```
@@ -63,7 +78,7 @@ bash run.sh test.py config/config_mpii.yaml
 ```
 
 ### Result
-After training or test, you can find the result from the `save_path` in `config_mpii.yaml`. 
+After training or test, you can find the result from the `$save_path$` in `config_mpii.yaml`. 
 
 
 ## Citation
@@ -77,12 +92,30 @@ If you use our code, please cite:
 	year = {2015}
 }
 
-@inproceedings{Cheng2021Survey,
-	title={Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark},
-	author={Yihua Cheng, Haofei Wang, Yiwei Bao, Feng Lu},
-	booktitle={arxiv}
-	year={2021}
+@article{Cheng2021Survey,
+        title={Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark},
+        author={Yihua Cheng and Haofei Wang and Yiwei Bao and Feng Lu},
+        journal={arXiv preprint arXiv:2104.12668},
+        year={2021}
 }
 ```
 ## Contact 
 Please email any questions or comments to [email protected].
+
+## Reference
+
+1. MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation
+2. EYEDIAP Database: Data Description and Gaze Tracking  Evaluation Benchmarks
+3. Learning-by-Synthesis for Appearance-based 3D Gaze Estimation
+3. Gaze360: Physically Unconstrained Gaze Estimation in the Wild
+5. ETH-XGaze: A Large Scale Dataset for Gaze Estimation under Extreme Head Pose and Gaze Variation
+6. Appearance-Based Gaze Estimation in the Wild  
+7. Appearance-Based Gaze Estimation Using Dilated-Convolutions
+8. RT-GENE: Real-Time Eye Gaze Estimation in Natural Environments
+9. It’s written all over your face: Full-face appearance-based gaze estimation
+10. A Coarse-to-fine Adaptive Network for Appearance-based Gaze Estimation
+11. Eye Tracking for Everyone
+12. Adaptive Feature Fusion Network for Gaze Tracking in Mobile Tablets
+13. On-Device Few-Shot Personalization for Real-Time Gaze Estimation
+14. A Generalized and Robust Method Towards Practical Gaze Estimation on Smart Phone
+