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A Dual-Network Progressive Approach to Weakly Supervised Object Detection, ACM MM 2017
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README.md

A Dual-Network Progressive Approach to Weakly Supervised Object Detection

By Xuanyi Dong, Deyu Meng, Fan Ma, Yi Yang. This paper is accepted by ACM Multimedia 2017.

Introduction

Dual-Network is a weakly supervised object detection framework leveraging deep CNN models.

This project is modified on the Matlab code of R-FCN and Fast R-CNN.

License

Dual-Network is released under the MIT License (refer to the LICENSE file for details).

Resources & Preparation

  1. ImageNet-pretrained networks: Google Drive. Please save the models into the corresponding sub-directory of models/pre_trained_models.
  2. The initial pseudo labels for PASCAL VOC 2007 by ContextLocNet : Google Drive. Please save and extract it into data.
  3. The pre-computed region proposals: Google Drive. Please save and extract it into data.
  4. Download the PASCAL VOC 2007 data into datasets, following the README in datasets.
  5. Compile Caffe located in external/caffe.
  6. Run dual_build.m to complie the nms mex functions.
  7. Run startup.m to add necessary paths.

Training & Testing

  • [TODO] re-organize the experiment codes.

Citing Dual-Network

If you find Dual-Network useful in your research, please consider citing:

@inproceedings{dong2017dual,
    title={A Dual-Network Progressive Approach to Weakly Supervised Object Detection},
    author={Dong, Xuanyi and Meng, Deyu and Ma, Fan and Yang, Yi},
    booktitle={Proceedings of the 2017 ACM on Multimedia Conference},
    pages={279--287},
    year={2017},
    organization={ACM}
}
@inproceedings{kantorov2016,
    title = {ContextLocNet: Context-aware Deep Network Models for Weakly Supervised Localization},
    author = {Kantorov, V., Oquab, M., Cho M. and Laptev, I.},
    booktitle = {Proc. European Conference on Computer Vision (ECCV), 2016},
    year = {2016}
}
@article{dai16rfcn,
    Author = {Jifeng Dai, Yi Li, Kaiming He, Jian Sun},
    Title = {{R-FCN}: Object Detection via Region-based Fully Convolutional Networks},
    Journal = {arXiv preprint arXiv:1605.06409},
    Year = {2016}
}
@inproceedings{girshick2015fast,
    title={Fast R-CNN},
    author={Girshick, Ross},
    booktitle={Proceedings of the IEEE international conference on computer vision},
    pages={1440--1448},
    year={2015}
}
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