AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation
A pytorch-version implementation codes of paper:
“AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation”,
which is accepted in BMVC 2021.
Prerequisites
- Python 3.8
- Pytorch 1.9
- fvcore
- numpy, pandas, matplotlib, tensorboardX etc.
Download Features
3D Resnet-50 features extracted from rescaled videos of ActivityNet-1.3 can be downloaded
[here](will be updated soon).
Training and Testing of AEI
Default configurations of AEI are stored in config/defaults.py.
The modified configurations are stored in config/*.yaml for training and testing of AEI on different datasets (ActivityNet-1.3 and THUMOS-14).
We can also modify configurations through commandline arguments.
- To train AEI on TAPG task of ActivityNet-1.3 with 1 GPU:
python main.py --cfg-file config/anet_proposals.yaml MODE 'training' GPU_IDS [0]
- To evaluate AEI on validation set of ActivityNet-1.3 with 1 GPU:
python main.py --cfg-file config/anet_proposals.yaml MODE 'validation' GPU_IDS [0]
Reference
This implementation is partly based on this pytorch-implementation of BMN.
paper:[AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation](will update soon)
