DenseVideoCaptioning

Tensorflow Implementation of the Paper Bidirectional Attentive Fusion with Context Gating for Dense Video Captioning by Jingwen Wang et al. in CVPR 2018.

Citation

@inproceedings{wang2018bidirectional,
  title={Bidirectional Attentive Fusion with Context Gating for Dense Video Captioning},
  author={Wang, Jingwen and Jiang, Wenhao and Ma, Lin and Liu, Wei and Xu, Yong},
  booktitle={CVPR},
  year={2018}
}

Data Preparation

Please download annotation data and C3D features from the website ActivityNet Captions. The ActivityNet C3D features with stride of 64 frames (used in my paper) can be found in https://drive.google.com/open?id=1UquwlUXibq-RERE8UO4_vSTf5IX67JhW.

Please follow the script dataset/ActivityNet_Captions/preprocess/anchors/get_anchors.py to obtain clustered anchors and their pos/neg weights (for handling imbalance class problem). I already put the generated files in dataset/ActivityNet_Captions/preprocess/anchors/.

Please follow the script dataset/ActivityNet_Captions/preprocess/build_vocab.py to build word dictionary and to build train/val/test encoded sentence data.

Hyper Parameters

The configuration (from my experiments) is given in opt.py, including model setup, training options, and testing options. You may want to set max_proposal_num=1000 if saving valiation time is not the first priority.

Training

Train dense-captioning model using the script train.py.

First pre-train the proposal module (you may need to slightly modify the code to support batch size of 32, using batch size of 1 could lead to unsatisfactory performance). The pretrained proposal model can be found in https://drive.google.com/drive/folders/1IeKkuY3ApYe_QpFjarweRb2MTJKTCOLa. Then train the whole dense-captioning model by setting train_proposal=True and train_caption=True. To understand the proposal module, I refer you to the original SST paper and also my tensorflow implementation of SST.

Prediction

Follow the script test.py to make proposal predictions and to evaluate the predictions. Use max_proposal_num=1000 to generate .json test file and then use script "python2 evaluate.py -s [json_file] -ppv 100" to evaluate the performance (the joint ranking requres to drop items that are less confident).

Evaluation

Please note that the official evaluation metric has been updated (Line 194). In the paper, old metric is reported (but still, you can compare results from different methods, all CVPR-2018 papers report old metric).

Pre-trained Model & Results

[Deprecated] The predicted results for val/test set can be found here.

The pre-trained model and validation/test prediction can be found here. On validation set the model obtained 9.77 METEOR score using evaluate_old.py and 5.42 METEOR score using evaluate.py. On test set the model obtained 4.49 METEOR score returned by the ActivityNet server.

Dependencies

tensorflow==1.0.1

python==2.7.5

Other versions may also work.

NOTE:

1. Due to large file constraint, you may need to download data/paraphrase-en.gz here and put it in densevid_eval-master/coco-caption/pycocoevalcap/meteor/data/.