Accepted to ESEC/FSE2020
This repository includes all code/pretrained models in our paper, namely Faster RCNN, YOLO v3, CenterNet, Xianyu, REMAUI and our model
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Video: YouTube
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Dataset: Our dataset is based on Rico
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Pretrained Models, Data Splitting and Processed Dataset: Zenodo
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Tool: http://uied.online
All code is tested under Ubuntu 16.04, Cuda 9.0, PyThon 3.9, torch 1.12.1, Nvidia 1080 Ti
See GitHub
See the corresponding folder in this repository. Each folder contains an individual README file.
Faster RCNN, YOLOv3, CenterNet, Xianyu
For REAMUI, see pix2app
The implementations of Faster RCNN, YOLO v3, CenterNet and REMAUI are based on the following GitHub Repositories. Thank for the works.
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Faster RCNN: https://github.com/jwyang/faster-rcnn.pytorch/tree/pytorch-1.0
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CenterNet: https://github.com/Duankaiwen/CenterNet
We implement Xianyu based on their technical blog
- XianYu: https://laptrinhx.com/ui2code-how-to-fine-tune-background-and-foreground-analysis-2293652041/
COCOApi: https://github.com/cocodataset/cocoapi
@inproceedings{chen2020object,
title={Object Detection for Graphical User Interface: Old Fashioned or Deep Learning or a Combination?},
author={Chen, Jieshan and Xie, Mulong and Xing, Zhenchang and Chen, Chunyang and Xu, Xiwei, Zhu, Liming and Guoqiang Li},
booktitle={Proceedings of the 2020 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering},
year={2020},
publisher = "ACM",
address = "New York, NY",
doi = "10.1145/3368089.3409691",
}
@inbook{UIED,
author = {Xie, Mulong and Feng, Sidong and Xing, Zhenchang and Chen, Jieshan and Chen, Chunyang},
title = {UIED: A Hybrid Tool for GUI Element Detection},
year = {2020},
isbn = {9781450370431},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3368089.3417940},
booktitle = {Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering},
pages = {1655–1659},
numpages = {5}
}