Awesome
DCFNet
Accepted paper in ICCV2021:
Dynamic Context-Sensitive Filtering Network for Video Salient Object Detection
Miao Zhang, Jie Liu, Yifei Wang, Yongri Piao, Shunyu Yao, Wei Ji, Jingjing Li, Huchuan Lu, Zhongxuan Luo.
Prerequisites
- Ubuntu 16
- PyTorch 1.6.0
- CUDA 10.1
- Cudnn 7.5.0
- Python 3.6
VSOD Training and Testing Datasets
VSOD Training dataset
VSOD Training dataset. Code: oip1
VSOD Testing dataset
VSOD Testing dataset. Code: oip1
Train/Test
Testing
- Firstly, you need to download the 'VSOD Testing Dataset' and the pretrained checkpoint we provided (video_current_best_model.pth. Code: oip1).
- Secondly, you need to set dataset path and checkpoint name correctly, and set the param '--split' as "test" or 'val' in inference.py for generating saliency results.
- Finally, you can evaluate the saliency results by using the widely-used tool provided by DAVSOD.
- Or you can directly download the results we provided (DCFNet results. Code: oip1).
python inference.py
Training
- Firstly, you need to download the 'VSOD Training Dataset' and modify your path of training dataset
- Secondly, you can pretrain the ImageModel for DCFNet following the training settings in the paper
- Finally, you can train the VideoModel for DCFNet
python train.py
Contact Us
If you have any questions, please contact us (1605721375@mail.dlut.edu.cn; dilemma@mail.dlut.edu.cn).
Acknowledge
Thanks to the previous helpful works: