Source code for "MFNet: Multi-filter Directive Network for Weakly Supervised Salient Object Detection", accepted in ICCV-2021, poster.

Yongri Piao, Jian Wang, Miao Zhang and Huchuan Lu. IIAU-OIP Lab.

• Windows 10
• Torch 1.8.1
• CUDA 10.0
• Python 3.7.4
• other environment requirment can be found in requirments.txt

link: https://pan.baidu.com/s/1omTCChQFWwNFhQ79AVD8rg. code: oipw

link: https://pan.baidu.com/s/1PBzDP1Hnf3RIvpARmxn2yA. code: oipw

Case1: please refer to this repository.

Case2: We also upload ready-made pseudo labels in Training data (the link above), you can directly use our offered two kinds of pseudo labels for convenience. CAMs are also presented if you needed.

MF_code -- data -- DUTS-Train -- image -- 10553 samples

                -- ECSSD (not necessary) 
                
                -- pseudo labels -- label0_0 -- 10553 pseudo labels
                
                                 -- label1_0 -- 10553 pseudo labels

Run main.py

Here you can set ECCSD dataset as validation set for optimal results by setting --val to True, of course it is not necessary in our work.

Run test_code.py

You need to configure your desired testset in --test_root. Here you can also perform PAMR and CRF on saliency maps for a furthur refinements if you want, by setting --pamr and --crf to True. Noting that the results in our paper do not adopt these post-process for a fair comparison.

The evaluation code can be found in here.

We offer our saliency maps and checkpoints on various backbones (including DenseNet-169, ResNet-101, ResNet-50 and VGG-16) for more convenient comparison in the future. The results in our paper are all come from the model based on DenseNet-169, and we also highly recommend the following researchers adopt same backbone for a more fair and convenient comparison.

link: https://pan.baidu.com/s/1IRTEaEicYaCJ2TYjZV1lZA. code: oipw

link: https://pan.baidu.com/s/1lQm-MY4uwZZTW5NJTTXeyA. code: oipw

If you have any questions, please contact me: [[email protected]].

Thanks to pioneering helpful works:

• MSW: Multi-source weak supervision for saliency detection, CVPR2019, by Yu Zeng et al.
• SSSS: Single-stage Semantic Segmentation from Image Labels, CVPR2020, by Nikita Araslanov et al.