BSN++: Complementary Boundary Regressor with Scale-Balanced Relation Modeling for Temporal Action Proposal Generation
A pytorch-version implementation codes of paper:
“BSN++: Complementary Boundary Regressor with Scale-Balanced Relation Modeling for Temporal Action Proposal Generation”,
which is accepted in AAAI 2021.
Result
| AN | Recall |
|---|---|
| AR@1 | 33.7% |
| AR@5 | 47.8% |
| AR@10 | 55.0% |
| AR@100 | 75.3% |
| AUC | 66.74 |
Prerequisites
These code is implemented in Pytorch 1.5.1 + Python3 .
Download Datasets
The author rescaled the feature length of all videos
to same length 100, and he provided the rescaled feature at
BaiduCloud [Code:efy8].
Training and Testing of BSN++
All configurations of BSN++ are saved in opts.py, where you can modify training and model parameter.
- To train the BSN++:
python main.py --mode train
- To get the inference proposal of the validation videos and evaluate the proposals with recall and AUC:
python main.py --mode inference
Of course, you can complete all the process above in one line:
sh bsnpp.sh
Reference
This implementation largely borrows from BMN by JJBOY.
code:BMN
