TSI

This repo holds the official pytorch implementation of paper: "TSI: Temporal Scale Invariant Network for Action Proposal Generation", which is accepted in ACCV 2020.

• Author: Shuming Liu, Xu Zhao, Haisheng Su, Zhilan Hu

Environment

This code is built on pytorch1.10+CUDA11, but other version may also be fine.

To install the dependency: pip install numpy pandas easydict tqdm scipy h5py PyYAML

Data Preparation

Change the feature root and feature name in the config files, such as in config/anet/anet_tsn.yaml.

For ActivityNet dataset

TSN feature

• Download the provided feature in "BSN". The data path should be DATAPATH/features/tsn_anet/csv_mean_100/v_224E-VtB4k4.csv
• FEATURE.name in config should be tsn_anet

TSP feature

• Download the provided feature in "TSP". The data path should be DATAPATH/features/tsp_r2plus1d_34/csv_unresize/v_224E-VtB4k4.csv
• FEATURE.name in config should be tsp_r2plus1d_34

For THUMOS dataset

TSN feature

• use the provided feature in "G-TAD". The data path should be DATAPATH/features/tsn_gtad/rgb_train.h5
• FEATURE.name in config should be tsn_gtad

I3D feature in P-GCN

• use the provided feature in "P-GCN". The data path should be DATAPATH/features/i3d_pgcn_snippet8/RGB/
• FEATURE.name in config should be i3d_pgcn_snippet8

I3D feature in BU-TAL

• use the provided feature in "BU-TAL". The data path should be DATAPATH/features/i3d_butal_snippet4_clip16/video_validation_0000051.npy
• FEATURE.name in config should be i3d_butal_snippet4_clip16

For HACS dataset

SlowFast feature

• use the provided feature in "TCANet". The data path should be DATAPATH/features/slowfast101/pkl_unresize/0_0MMzh2E3U.pkl
• FEATURE.name in config should be slowfast101/pkl_unresize

Train and Test

Bash Run

bash train.sh {config_path} {GPU_num}

For example: bash train.sh configs/anet/anet_tsn.yaml 1

Step-by-Step Run

python scripts/train.py {config_path} {GPU_num}
python scripts/test.py  {config_path} {GPU_num} {checkpoint_path}
python scripts/post.py  {config_path}

• {GPU_num} is the GPU number used for training and inference.
• {config_path} is the path of config.
• {checkpoint_path} is the path of loading checkpoint. If empty, load the best loss checkpoint by default.

Performance

Pretrained weights and experiment outputs can be found in Google Drive.

ActivityNet 1.3

TSN feature provided by BSN

Method	AR@1	AR@5	AR@10	AR@100	AUC
BMN	33.60	49.28	56.71	75.33	67.26
TSI	32.86	49.69	57.47	75.47	68.24

TSP feature provided by TSP

Method	AR@1	AR@5	AR@10	AR@100	AUC
BMN	34.85	51.38	58.47	76.07	68.47
TSI	34.30	52.17	59.29	76.73	69.42

THUMOS14

TSN feature provided by G-TAD

Method	AR@50	AR@100	AR@200	AR@500	AR@1000	AUC
BMN	40.61	49.79	57.40	65.75	70.72	62.08
TSI	40.93	50.23	57.88	66.46	71.95	62.99

I3D feature provided by P-GCN

Method	AR@50	AR@100	AR@200	AR@500	AR@1000	AUC
BMN	33.76	42.70	50.85	59.83	65.36	56.18
TSI	38.17	46.26	53.98	62.93	67.81	59.31

I3D feature provided by BU-TAL

Method	AR@50	AR@100	AR@200	AR@500	AR@1000	AUC
BMN	40.93	49.99	56.92	64.66	68.93	61.20
TSI	41.51	50.49	57.86	65.71	70.08	62.21

HACS

SlowFast feature provided by TCANet

Method	AR@1	AR@5	AR@10	AR@100	AUC
BMN	19.90	40.00	49.22	70.52	61.66
TSI	19.38	41.13	50.87	71.83	63.25

Acknowledgment and Citation

We thank for the help of Tianwei Lin, Dongqi Wang.

If you find this work is useful in your research, please consider citing:

@inproceedings{liu2020tsi,
  title={TSI: Temporal Scale Invariant Network for Action Proposal Generation},
  author={Liu, Shuming and Zhao, Xu and Su, Haisheng and Hu, Zhilan},
  booktitle={Proceedings of the Asian Conference on Computer Vision},
  year={2020}
}

Contact

For any question, please contact sming256@gmail.com.