EMANet

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.

citation

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

@inproceedings{li19,
    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},   
    year={2019},   
}

table of contents

• Introduction
• Usage
• Ablation studies
• Comparisons with SOTAs

Introduction

Usage

Ablation Studies

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.

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

Tab 2. Comparisons on the PASCAL VOC test dataset. OS means the output stride for training. For test, output stride is set as 8.