This repository is for Expectation-Maximization Attention Networks for Semantic Segmentation (to appear in ICCV 2019, Oral presentation),

by Xia Li, Zhisheng Zhong, Jianlong Wu, Yibo Yang, Zhouchen Lin and Hong Liu from Peking University.

The source code is now available!


If you find EMANet useful in your research, please consider citing:

    author={Xia Li and Zhisheng Zhong and Jianlong Wu and Yibo Yang and Zhouchen Lin and Hong Liu},
    title={Expectation-Maximization Attention Networks for Semantic Segmentation},
    booktitle={International Conference on Computer Vision},   

table of contents


Self-attention mechanism has been widely used for various tasks. It is designed to compute the representation of each position by a weighted sum of the features at all positions. Thus, it can capture long-range relations for computer vision tasks. However, it is computationally consuming. Since the attention maps are computed w.r.t all other positions. In this paper, we formulate the attention mechanism into an expectation-maximization manner and iteratively estimate a much more compact set of bases upon which the attention maps are computed. By a weighted summation upon these bases, the resulting representation is low-rank and deprecates noisy information from the input. The proposed Expectation-Maximization Attention (EMA) module is robust to the variance of input and is also friendly in memory and computation. Moreover, we set up the bases maintenance and normalization methods to stabilize its training procedure. We conduct extensive experiments on popular semantic segmentation benchmarks including PASCAL VOC, PASCAL Context, and COCO Stuff, on which we set new records. EMA Unit


As so many peers have starred at this repo, I feel the great pressure, and try to release the code with high quality. That’s why I didn’t release it until today (Aug, 22, 2018). It’s known that the design of the code structure is not an easy thing. Different designs are suitable for different usage. Here, I aim at making research on Semantic Segmentation, especially on PASCAL VOC, more easier. So, I delete necessary encapsulation as much as possible, and leave over less than 10 python files. To be honest, the global variables in settings are not a good design for large project. But for research, it offers great flexibility. So, hope you can understand that

For research, I recommand seperatting each experiment with a folder. Each folder contains the whole project, and should be named as the experiment settings, such as ‘EMANet101.moving_avg.l2norm.3stages’. Through this, you can keep tracks of all the experiments, and find their differences just by the ‘diff’ command.


  1. Install the libraries listed in the ‘requirements.txt’
  2. Downloads images and labels of PASCAL VOC and SBD, decompress them together.
  3. Downloads the pretrained ResNet50 and ResNet101, unzip them, and put into the ‘models’ folder.
  4. Change the ‘DATA_ROOT’ in settings.py to where you place the dataset.
  5. Run sh clean.sh to clear the models and logs from the last experiment.
  6. Run python train.py for training and sh tensorboard.sh for visualization on your browser.
  7. Or you can download the pretraind model, put into the ‘models’ folder, and skip step 6.
  8. Run python eval.py for validation

Ablation Studies

The following results are referred from the paper. For this repo, it’s not strange to get even higer performance. If so, I’d like you share it in the issue. By now, this repo only provides the SS inference. I may release the code for MS and Flip latter.

Tab 1. Detailed comparisons with Deeplabs. All results are achieved with the backbone ResNet-101 and output stride 8. The FLOPs and memory are computed with the input size 513×513. SS: Single scale input during test. MS: Multi-scale input. Flip: Adding left-right flipped input. EMANet (256) and EMANet (512) represent EMANet withthe number of input channels for EMA as 256 and 512, respectively.

Method SS MS+Flip FLOPs Memory Params
ResNet-101 - - 190.6G 2.603G 42.6M
DeeplabV3 78.51 79.77 +63.4G +66.0M +15.5M
DeeplabV3+ 79.35 80.57 +84.1G +99.3M +16.3M
PSANet 78.51 79.77 +56.3G +59.4M +18.5M
EMANet(256) 79.73 80.94 +21.1G +12.3M +4.87M
EMANet(512) 80.05 81.32 +43.1G +22.1M +10.0M

To be note, the majority overheads of EMANets come from the 3x3 convs before and after the EMA Module. As for the EMA Module itself, its computation is only 1/3 of a 3x3 conv’s, and its parameter number is even smaller than a 1x1 conv.

Comparisons with SOTAs

Note that, for validation on the ‘val’ set, you just have to train 30k on the ‘trainaug’ set. But for test on the evaluation server, you should first pretrain on COCO, and then 30k on ‘trainaug’, and another 30k on the ‘trainval’ set.

Tab 2. Comparisons on the PASCAL VOC test dataset.

Method Backbone mIoU(\%)
GCN ResNet-152 83.6
RefineNet ResNet-152 84.2
Wide ResNet WideResNet-38 84.9
PSPNet ResNet-101 85.4
DeeplabV3 ResNet-101 85.7
PSANet ResNet-101 85.7
EncNet ResNet-101 85.9
DFN ResNet-101 86.2
Exfuse ResNet-101 86.2
IDW-CNN ResNet-101 86.3
SDN DenseNet-161 86.6
DIS ResNet-101 86.8
EMANet101 ResNet-101 87.7
DeeplabV3+ Xception-65 87.8
Exfuse ResNeXt-131 87.9
MSCI ResNet-152 88.0
EMANet152 ResNet-152 88.2

Code Borrowed From