[ICCV2023] D3G:Exploring Gaussian Prior for Temporal Sentence Grounding with Glance Annotation

Datasets

• Annotations. We adopt the glance annotation released by ViGA for training. Specifically, we adapt the glance annotation to our framework in json format ( see in dataset ).
• Features. As for video features, we utilize the publicly available features for fair comparison following MMN.

Please download the video features to directory dataset as follows.

dataset
├── Charades_STA
│   ├── vgg_rgb_features.hdf5
│   ├── glance_charades_train.json
│   ├── charades_test.json
├── ActivityNet
│   ├── sub_activitynet_v1-3.c3d.hdf5
│   ├── glance_train.json
│   ├── val.json
│   ├── test.json
├── TACoS
│   ├── tall_c3d_features.hdf5
│   ├── glance_train.json
│   ├── val.json
│   ├── test.json

Main Results

Charades-STA Dataset

Method	Rank1@0.5	Rank1@0.7	Rank5@0.5	Rank5@0.7
ViGA	36.56	16.10	48.90	25.86
D3G	41.64	19.60	79.25	49.30

ActivityNet Captions Dataset

Method	Rank1@0.3	Rank1@0.5	Rank1@0.7	Rank5@0.3	Rank5@0.5	Rank5@0.7
ViGA	59.78	35.39	16.25	72.19	53.19	32.69
D3G	58.25	36.68	18.54	87.84	74.21	52.47

TACoS Dataset

Method	Rank1@0.3	Rank1@0.5	Rank1@0.7	Rank5@0.3	Rank5@0.5	Rank5@0.7
ViGA	20.82	9.52	3.10	27.92	15.35	6.10
D3G	26.99	12.62	4.77	54.71	31.59	12.10

Training & Inference

cd scipts 
### charades 
sh charades_train.sh # train 
sh charades_test.sh # test 

### activitynet
sh anet_train.sh # train 
sh anet_test.sh # test 

### tacos 
sh tacos_train.sh  # train 
sh tacos_test.sh # test