A fast-running version：
Baidu Cloud: https://pan.baidu.com/s/1DzSTCFCwLmJ2BT0q104TXg code：5abn
Google Drive: https://drive.google.com/file/d/1ZHgdRs-yUzreSFOThOP00MIUVxeRoMYc/view?usp=sharing
The source code of our paper of IEEE Transactions on Image Processing(V2):
Exploring Rich and Efficient Spatial Temporal Interactions for Real Time Video Salient Object Detection
Because the code of V1 is relatively long to upload,
we re-implemented STVS based on the BBSNet and uploaded it to Baidu Cloud Disk.
link：https://pan.baidu.com/s/1tneKPmyvmMBPyv_meZmeiQ
code：3dqb
- The code is mainly for the second overhaul of the paper.
- The training data needs to be enhanced (Interval 2, 3, 4, 5; Rotation; Mirroring; Gaussian noise; Lighting changes; Scale changes, etc.)
- At present, there is another way to realize that 3 frames of images correspond to 3 outputs, and then sum of the losses of the 3 frames.(still in progress)
The source code of our manuscript submitted to IEEE Transactions on Image Processing(V1):
Exploring Rich and Efficient Spatial Temporal Interactions for Real Time Video Salient Object Detection
- CUDA v8.0, cudnn v7.0
- python 3.5
- pytorch 0.4.1
- torchvision
- numpy
- Cython
- GPU: NVIDIA GeForce GTX 1080 Ti
All results on Davis-T, SegTrack-V2, Visal, FBMS-T and DAVSOD-T datasets of our method are availabled from
baidu cloud: https://pan.baidu.com/s/1J9yYBeMXmaUvGQ1aAbBUYA, extraction: 45gi.
Results on VOS-test dataset:https://pan.baidu.com/s/1FbqHNlqP07BL5k0sg4Wyaw, extraction: 16ep.
You can download the trained model from baidu cloud: https://pan.baidu.com/s/16wPfMNPjDlnwWx4xuM8R3Q,
extraction: 7w7p.
a. Please first download the model.
b. Please put test images under .\DataSet.
d. Please put the model under folder .\model.
e. Run demo.py.
f. Results can be found in .\resutls.
- Using the entire training set to pretrain the spatial branch.
- Finetuing the whole spatiotemporal model using all training set.