Update RAEDME.md

Author: Bin Xiao

README.md

@@ -1,7 +1,7 @@
-# Deep High-Resolution Representation Learning for Human Pose Estimation(to appear in CVPR2019)
+# Deep High-Resolution Representation Learning for Human Pose Estimation(accepted to CVPR2019)
 
 ## Introduction
-This is an official pytorch implementation of [*Deep High-Resolution Representation Learning for Human Pose Estimation*](https://arxiv.org/). 
+This is an official pytorch implementation of [*Deep High-Resolution Representation Learning for Human Pose Estimation*](https://arxiv.org/abs/1902.09212). 
 In this work, we are interested in the human pose estimation problem with a focus on learning reliable high-resolution representations. Most existing methods **recover high-resolution representations from low-resolution representations** produced by a high-to-low resolution network. Instead, our proposed network **maintains high-resolution representations** through the whole process.
 We start from a high-resolution subnetwork as the first stage, gradually add high-to-low resolution subnetworks one by one to form more stages, and connect the mutli-resolution subnetworks **in parallel**. We conduct **repeated multi-scale fusions** such that each of the high-to-low resolution representations receives information from other parallel representations over and over, leading to rich high-resolution representations. As a result, the predicted keypoint heatmap is potentially more accurate and spatially more precise. We empirically demonstrate the effectiveness of our network through the superior pose estimation results over two benchmark datasets: the COCO keypoint detection dataset and the MPII Human Pose dataset. </br>
 
@@ -214,6 +214,10 @@ python tools/train.py \
     --cfg experiments/coco/hrnet/w32_256x192_adam_lr1e-3.yaml \
 ```
 
+
+### Other applications
+Many other dense prediction tasks, such as segmentation, face alignment and object detection, etc. have been benefited by HRNet. More information can be found at [Deep High-Resolution Representation Learning](https://jingdongwang2017.github.io/Projects/HRNet/).
+
 ### Citation
 If you use our code or models in your research, please cite with:
 ```