Update README.md

Author: hangzhaomit

README.md

@@ -36,8 +36,10 @@ For the task of semantic segmentation, it is good to keep aspect ratio of images
 
 <sup>*Now the batch size of a dataloader always equals to the number of GPUs*, each element will be sent to a GPU. It is also compatible with multi-processing. Note that the file index for the multi-processing dataloader is stored on the master process, which is in contradict to our goal that each worker maintains its own file list. So we use a trick that although the master process still gives dataloader an index for `__getitem__` function, we just ignore such request and send a random batch dict. Also, *the multiple workers forked by the dataloader all have the same seed*, you will find that multiple workers will yield exactly the same data, if we use the above-mentioned trick directly. Therefore, we add one line of code which sets the defaut seed for `numpy.random` before activating multiple worker in dataloader.</sup>
 
-### An Efficient and Effective Framework: UPerNet
-UPerNet is a model based on Feature Pyramid Network (FPN) and Pyramid Pooling Module (PPM). It doesn't need dilated convolution, an operator that is time-and-memory consuming. *Without bells and whistles*, it is comparable or even better compared with PSPNet, while requiring much shorter training time and less GPU memory (e.g., you cannot train a PSPNet-101 on TITAN Xp GPUs with only 12GB memory, while you can train a UPerNet-101 on such GPUs). Thanks to the efficient network design, we will soon open source stronger models of UPerNet based on ResNeXt that is able to run on normal GPUs. Please refer to [UperNet](https://arxiv.org/abs/1807.10221) for details.
+### State-of-the-Art models
+- **PSPNet** is scene parsing network that aggregates global representation with Pyramid Pooling Module (PPM). It is the winner model of ILSVRC'16 MIT Scene Parsing Challenge. Please refer to [https://arxiv.org/abs/1612.01105](https://arxiv.org/abs/1612.01105) for details.
+- **UPerNet** is a model based on Feature Pyramid Network (FPN) and Pyramid Pooling Module (PPM). It doesn't need dilated convolution, an operator that is time-and-memory consuming. *Without bells and whistles*, it is comparable or even better compared with PSPNet, while requiring much shorter training time and less GPU memory. Please refer to [https://arxiv.org/abs/1807.10221](https://arxiv.org/abs/1807.10221) for details.
+- **HRNet** is a recently proposed model that retains high resolution representations throughout the model, without the traditional bottleneck design. It achieves the SOTA performance on a series of pixel labeling tasks. Please refer to [https://arxiv.org/abs/1904.04514](https://arxiv.org/abs/1904.04514) for details.
 
 
 ## Supported models
@@ -220,6 +222,7 @@ python3 eval_multipro.py --gpus GPUS --cfg config/ade20k-resnet50dilated-ppm_dee
 * Evaluate UPerNet101
 ```bash
 python3 eval_multipro.py --gpus GPUS --cfg config/ade20k-resnet101-upernet.yaml
+```
 
 ## Reference