Prerequisite: Python 3.6 and torch 1.1.0 and tqdm
Download RAP(v2) dataset and annotation then put in dataset directory
( If you simply want to run the demo code without further modification, you might skip this step by downloading the weight file from
Baidu Yun with password "5z1j" and put the model_best.pth.tar into directory /checkpoint/ then run
python demo.py )
python transform_rap2.py (transform data)
python glove.py (word2vec)
python adj.py (Adjacency matrix)
python train.py (weight file will locate in checkpoint directory)
| method | mA | accuracy | precision | recall | F1 |
|---|---|---|---|---|---|
| ACN | 69.66 | 62.61 | 80.12 | 72.26 | 75.98 |
| DeepMar | 73.79 | 62.02 | 74.92 | 76.21 | 75.56 |
| HP-Net | 76.12 | 65.39 | 77.33 | 78.79 | 78.05 |
| JRL | 77.81 | - | 78.11 | 78.98 | 78.58 |
| VeSPa | 77.70 | 67.35 | 79.51 | 79.67 | 79.59 |
| Ours | 75.97 | 68.99 | 81.48 | 79.97 | 80.72 |