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CSMGAN

Code for ACM MM2020 paper

Jointly Cross- and Self-Modal Graph Attention Network for Query-Based Moment Localization
[Paper]

Main Results

Activity Caption
R@1, IoU=0.3 R@1, IoU=0.5 R@1, IoU=0.7 R@5, IoU=0.3 R@5, IoU=0.5 R@5, IoU=0.7
68.52 49.11 29.15 87.68 77.43 59.63
TACoS
R@1, IoU=0.1 R@1, IoU=0.3 R@1, IoU=0.5 R@5, IoU=0.1 R@5, IoU=0.3 R@5, IoU=0.5
42.74 33.90 27.09 68.97 53.98 41.22
Charades-STA
R@1, IoU=0.5 R@1, IoU=0.7 R@5, IoU=0.5 R@5, IoU=0.7
60.04 37.34 89.01 61.85
DiDeMo
R@1, IoU=0.5 R@1, IoU=0.7 R@5, IoU=0.5 R@5, IoU=0.7
29.44 19.16 70.77 41.61

Prerequisites

  • Python 3.6
  • Pytorch >= 0.4.0

Preparation

Evaluation

$ python main.py --word2vec-path /yourpath/glove_model.bin --dataset ActivityNet --feature-path /yourpath/ActivityCaptions/ActivityC3D --train-data data/activity/train_data_gcn.json --val-data data/activity/val_data_gcn.json --test-data data/activity/test_data_gcn.json --max-num-epochs 20 --dropout 0.2 --warmup-updates 300 --warmup-init-lr 1e-06 --lr 8e-4 --num-heads 4 --num-gcn-layers 2 --num-attn-layers 2 --weight-decay 1e-7 --evaluate --model-load-path ./models_activity/model_6852
$ python main.py --word2vec-path /yourpath/glove_model.bin --dataset TACOS --feature-path /yourpath/TACOS/TACOS --train-data data/tacos/TACOS_train_gcn.json --val-data data/tacos/TACOS_val_gcn.json --test-data data/tacos/TACOS_test_gcn.json --max-num-epochs 40 --dropout 0.2 --warmup-updates 300 --warmup-init-lr 1e-07 --lr 4e-4 --num-heads 4 --num-gcn-layers 2 --num-attn-layers 2 --weight-decay 1e-8 --evaluate --model-saved-path models_tacos --batch-size 64 --model-load-path ./models_tacos/model_4274

Citation

If you use this code please cite:

@inproceedings{liu2020jointly,
    title={Jointly Cross- and Self-Modal Graph Attention Network for Query-Based Moment Localization},
    author={Liu, Daizong and Qu, Xiaoye and Liu, Xiaoyang and Dong, Jianfeng and Zhou, Pan and Xu, Zichuan},
    booktitle={Proceedings of the 28th ACM International Conference on Multimedia (MM’20)},
    year={2020}
}