Space-time Memory Networks for Video Object Segmentation with User Guidance

Seoung Wug Oh, Joon-Young Lee, Ning Xu, Seon Joo Kim

PAMI

[pami paper] [iccv19 paper]

- Requirements

• python 3.6
• pytorch 1.0.1.post2
• numpy, opencv, pillow, matplotlib
• PyQt5
• qdarkstyle
• davisinteractive
• etc

- How to Use

Download weights

Place it the same folder with demo scripts

wget -O e120.pth "https://www.dropbox.com/s/nvor7d4d1wk8nte/e120.pth?dl=1"

Run

python gui.py 

- Quantitative Evaluation

This repository contains a software only for demonstration. For the quantitative evaluation, we used the DAVIS framework. For the comparison with our model used for DAVIS Interactive VOS Challenge 2019 (https://davischallenge.org/challenge2019/interactive.html), please use evaluation summary obtained from the DAVIS framework. [Download link (DAVIS-17-val)]. The timing in the paper is measured using a single 2080Ti GPU.

- Reference

If you find our paper and repo useful, please cite our paper. Thanks!

Space-time Memory Networks for Video Object Segmentation with User Guidance
Seoung Wug Oh, Joon-Young Lee, Ning Xu, Seon Joo Kim
PAMI

Video Object Segmentation using Space-Time Memory Networks
Seoung Wug Oh, Joon-Young Lee, Ning Xu, Seon Joo Kim
ICCV 2019

- Related Project

Fast User-Guided Video Object Segmentation by Interaction-and-Propagation Networks
Seoung Wug Oh, Joon-Young Lee, Ning Xu, Seon Joo Kim
CVPR 2019

[paper] [github]

- Terms of Use

This software is for non-commercial use only. The source code is released under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) Licence (see this for details)