- The PyTroch 0.4.1 version is available [here](https://github.com/HRNet/HRNet-Semantic-Segmentation/tree/master).
## News
-[2020/07] Our paper is accepted by ECCV 2020: [Object-Contextual Representations for Semantic Segmentation](https://arxiv.org/pdf/1909.11065.pdf)
-[2020/07] Our paper is accepted by ECCV 2020: [Object-Contextual Representations for Semantic Segmentation](https://arxiv.org/pdf/1909.11065.pdf). Notably, the reseachers from Nvidia set a new state-of-the-art performance on Cityscapes leaderboard: [85.4%](https://www.cityscapes-dataset.com/method-details/?submissionID=7836) via combining our HRNet + OCR with a new [hierarchical mult-scale attention scheme](https://arxiv.org/abs/2005.10821).
-[2020/03/13] Our paper is accepted by TPAMI: [Deep High-Resolution Representation Learning for Visual Recognition](https://arxiv.org/pdf/1908.07919.pdf).
- Thanks Google and UIUC researchers. A modified HRNet combined with semantic and instance multi-scale context achieves SOTA panoptic segmentation result on the Mapillary Vista challenge. See [the paper](https://arxiv.org/pdf/1910.04751.pdf).
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@@ -18,9 +18,27 @@
This is the official code of [high-resolution representations for Semantic Segmentation](https://arxiv.org/abs/1904.04514).
We augment the HRNet with a very simple segmentation head shown in the figure below. We aggregate the output representations at four different resolutions, and then use a 1x1 convolutions to fuse these representations. The output representations is fed into the classifier. We evaluate our methods on three datasets, Cityscapes, PASCAL-Context and LIP.
Besides, we further combine HRNet with [Object Contextual Representation](https://arxiv.org/pdf/1909.11065.pdf) and achieve higher performance on the three datasets. The code of HRNet+OCR is contained in this branch.
<!--  -->
<figure>
<text-align:center;>
<imgsrc="./figures/seg-hrnet.png"alt="hrnet"title="Framework of Object Contextual Representation"width="900"height="200"/>
<figcaption>Fig.1 - An example of a high-resolution network. Only the main body is illustrated, and the stem (two stride-2 3 × 3 convolutions) is not included.
There are four stages. The 1st stage consists of high-resolution convolutions. The 2nd (3rd, 4th) stage repeats two-resolution (three-resolution,
four-resolution) blocks.
</figcaption>
</figure>
Besides, we further combine HRNet with [Object Contextual Representation](https://arxiv.org/pdf/1909.11065.pdf) and achieve higher performance on the three datasets. The code of HRNet+OCR is contained in this branch. We illustrate the overall framework of OCR in the Figure as shown below:
<figure>
<text-align:center;>
<imgsrc="./figures/OCR.PNG"alt="OCR"title="Framework of Object Contextual Representation"width="900"height="200"/>
<figcaption>Fig.2 - Illustrating the pipeline of OCR. (i) form the soft object regions in the
pink dashed box. (ii) estimate the object region representations in the purple dashed box.
(iii) compute the object contextual representations and the augmented representations
in the orange dashed box.
</figcaption>
</figure>
## Segmentation models
The models are initialized by the weights pretrained on the ImageNet. You can download the pretrained models from https://github.com/HRNet/HRNet-Image-Classification. *Slightly different, we use align_corners = True for upsampling in HRNet*.
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@@ -255,13 +273,10 @@ If you find this work or code is helpful in your research, please cite:
@article{YuanCW19,
title={Object-Contextual Representations for Semantic Segmentation},
author={Yuhui Yuan and Xilin Chen and Jingdong Wang},
journal = {CoRR},
volume = {abs/1909.11065},
year={2019}
author={Yuan, Yuhui and Chen, Xilin and Wang, Jingdong},
