Code for SYSU submission to ActivityNet Challenge 2020 (Task2: Dense Video Captioning). Our approach follows a two-stage pipeline: first, we extract a set of temporal event proposals; then we propose a multi-event captioning model to capture the event-level temporal relationships and effectively fuse the multi-modal information.

We won the 2nd place and the technical paper is available at arxiv.

1. Python 3.6.2
2. CUDA 10.0, PyTorch 1.2.0 (may work on other versions but has not been tested)
3. other modules, run pip install -r requirement.txt

• ActivityNet video features. We use TSN features following this repo. You can follow the "Data Preparation" section to download feature files, then decompress and move them into ./data/resnet_bn.

• Download annotation files and pre-generated proposal files from Google Drive, and place them into ./data. For the proposal generation, please refer to DBG and ESGN.

• Build vocabulary file. Run python misc/build_vocab.py.

• (Optional) You can also test the code based on C3D feature. Download C3D feature files (sub_activitynet_v1-3.c3d.hdf5) from here. Convert the h5 file into npy files and place them into ./data/c3d.

• Training

# first, train the model with cross-entropy loss 
cfg_file_path=cfgs/tsrm_cmg_hrnn.yml
python train.py --cfg_path $cfg_file_path

# Afterward, train the model with reinforcement learning on enlarged training set
cfg_file_path=cfgs/tsrm_cmg_hrnn_RL_enlarged_trainset.yml
python train.py --cfg_path $cfg_file_path

training logs and generated captions are in this folder ./save.

• Evaluation

# evaluation with ground-truth proposals (small val set with 1000 videos)
result_folder=tsrm_cmg_hrnn_RL_enlarged_trainset
val_caption_file=data/captiondata/expand_trainset/val_1.json
python eval.py --eval_folder $result_folder --eval_caption_file $val_caption_file

# evaluation with learnt proposals (small val set with 1000 videos)
result_folder=tsrm_cmg_hrnn_RL_enlarged_trainset
lnt_tap_json=data/generated_proposals/tsn_dbg_esgn_valset_num4717.json
python eval.py --eval_folder $result_folder --eval_caption_file $val_caption_file --load_tap_json $lnt_tap_json

# evaluation with ground-truth proposals (standard val set with 4917 videos)
result_folder=tsrm_cmg_hrnn
python eval.py --eval_folder $result_folder

# evaluation with learnt proposals (standard val set with 4917 videos)
result_folder=tsrm_cmg_hrnn
lnt_tap_json=data/generated_proposals/tsn_dbg_esgn_valset_num4717.json
python eval.py --eval_folder $result_folder --load_tap_json $lnt_tap_json

• Testing

python eval.py --eval_folder tsrm_cmg_hrnn_RL_enlarged_trainset \
 --load_tap_json data/generated_proposals/tsn_dbg_esgn_testset_num5044.json\
 --eval_caption_file data/captiondata/fake_test_anno.json

We also provide the config files of some baseline models. Please see this folder ./cfgs for details.

We provide a pre-trained model from here. You can directly download model-best-RL.pth and info.json and place them into ./save/tsrm_cmg_hrnn_RL_enlarged_trainset, then run the above code for fast evaluation. On the small validation set (1000 videos), this model achieves a 14.51/10.14 METEOR with ground-truth/learnt proposals.

PDVC (ICCV 2021): A simple yet effective dense video captioning method, which integrates the proposal generation and captioning generation into a parallel decoding manner.

If you find this repo helpful to your research, please consider citing:

@article{wang2020dense,
  title={Dense-Captioning Events in Videos: SYSU Submission to ActivityNet Challenge 2020},
  author={Wang, Teng and Zheng, Huicheng and Yu, Mingjing},
  journal={arXiv preprint arXiv:2006.11693},
  year={2020}
}

• Awesome ImageCaptioning.pytorch project.
• Official implementation of "Weakly Supervised Dense Event Captioning in Videos".