panmyuan / cvpr18_detect_globally_refine_locally Goto Github PK

This package has the source code for the paper "Detect Globally, Refine Locally: A Novel Approach to Saliency Detection" (CVPR18).

• The paper can be found in Baidu drive or Google drive.

Train

• For Global Localizaiton Network (GLN), using the code in ./stage1/train/ for training. The example images are given in ./stage1/train/data/. Download the initialized model from Baidu drive or Google drive.
• For Boundary Refinement Network (BRN), using the code in ./stage2/train/ for training. After finishing the training process of the GLN, then run the code in ./stage1/test/test.m to generate saliency maps of GLN. The examples images are given in ./stage2/train/data/. Each image in the training set, including the saliency map generated by the GLN, the original RGB image, the ground truth, should be resize to 480 * 480 by the 'nearest' method. Using the code in ./stage2/train/init/generate_train.m to generate the initialization model.

Test

• Download our trained model from Baidu drive or Google drive.
• Run ./stage1/test/test.m to generate saliency maps of Global Localizaiton Network (GLN).
• Run ./stage2/test/test.m to generate saliency maps of Boundary Refinement Network (BRN). The saliency maps of GLN will serve as the input of BRN.

The saliency maps on 10 datasets including ECSSD, PASCAL-S, SOD, SED1, SED2, MSRA, DUT-OMRON, THUR15K, HKU-IS and DUTS can be found in the following links.

GLN: Baidu drive or Google drive.

BRN: Baidu drive or Google drive.

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

 @inproceedings{wang2018detect,
   title={Detect Globally, Refine Locally: A Novel Approach to Saliency Detection},
   author={Wang, Tiantian and Zhang, Lihe and Wang, Shuo and Lu, Huchuan and Yang, Gang and Ruan, Xiang and Borji, Ali},
   booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
   pages={3127--3135},
   year={2018}
 }

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