Skip to content

Latest commit

 

History

History
195 lines (159 loc) · 9.44 KB

README.md

File metadata and controls

195 lines (159 loc) · 9.44 KB
Raw

PDVC

PWC PWC

Official implementation for End-to-End Dense Video Captioning with Parallel Decoding (ICCV 2021)

[paper] [valse论文速递(Chinese)]

This repo supports:

  • two video captioning tasks: dense video captioning and video paragraph captioning
  • two datasets: ActivityNet Captions and YouCook2
  • video features containing C3D, TSN, and TSP.
  • visualization of the generated captions of your own videos

Table of Contents:

Updates

  • (2021.11.19) add code for running PDVC on raw videos and visualize the generated captions (support Chinese and other non-English languages)
  • (2021.11.19) add pretrained models with TSP features. It achieves 9.03 METEOR(2021) and 6.05 SODA_c, a very competitive results on ActivityNet Captions without self-critical sequence training.
  • (2021.08.29) add TSN pretrained models and support YouCook2

Introduction

PDVC is a simple yet effective framework for end-to-end dense video captioning with parallel decoding (PDVC), by formulating the dense caption generation as a set prediction task. Without bells and whistles, extensive experiments on ActivityNet Captions and YouCook2 show that PDVC is capable of producing high-quality captioning results, surpassing the state-of-the-art methods when its localization accuracy is on par with them. pdvc.jpg

Preparation

Environment: Linux, GCC>=5.4, CUDA >= 9.2, Python>=3.7, PyTorch>=1.5.1

  1. Clone the repo
git clone --recursive https://github.com/ttengwang/PDVC.git
  1. Create vitual environment by conda
conda create -n PDVC python=3.7
source activate PDVC
conda install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=10.1 -c pytorch
conda install ffmpeg
pip install -r requirement.txt
  1. Compile the deformable attention layer (requires GCC >= 5.4).
cd pdvc/ops
sh make.sh

Running PDVC on Your Own Videos

Download a pretrained model (GoogleDrive) with TSP features and put it into ./save. Then run:

video_folder=visualization/videos
output_folder=visualization/output
pdvc_model_path=save/anet_tsp_pdvc/model-best.pth
output_language=en
bash test_and_visualize.sh $video_folder $output_folder $pdvc_model_path $output_language

check the $output_folder, you will see a new video with embedded captions. Note that we generate non-English captions by translating the English captions by GoogleTranslate. To produce chinese captions, set output_language=zh-cn. For other language support, find the abbreviation of your language at this url, and you also may need to download a font supporting your language and put it into ./visualization.

demo.gifdemo.gif

Training and Validation

Download Video Features

cd data/anet/features
bash download_anet_c3d.sh
# bash download_anet_tsn.sh
# bash download_i3d_vggish_features.sh
# bash download_tsp_features.sh

Dense Video Captioning

  1. PDVC with learnt proposals
# Training
config_path=cfgs/anet_c3d_pdvc.yml
python train.py --cfg_path ${config_path} --gpu_id ${GPU_ID}
# The script will evaluate the model for every epoch. The results and logs are saved in `./save`.

# Evaluation
eval_folder=anet_c3d_pdvc # specify the folder to be evaluated
python eval.py --eval_folder ${eval_folder} --eval_transformer_input_type queries --gpu_id ${GPU_ID}
  1. PDVC with ground-truth proposals
# Training
config_path=cfgs/anet_c3d_pdvc_gt.yml
python train.py --cfg_path ${config_path} --gpu_id ${GPU_ID}

# Evaluation
eval_folder=anet_c3d_pdvc_gt
python eval.py --eval_folder ${eval_folder} --eval_transformer_input_type gt_proposals --gpu_id ${GPU_ID}

Video Paragraph Captioning

  1. PDVC with learnt proposals
# Training
config_path=cfgs/anet_c3d_pdvc.yml
python train.py --cfg_path ${config_path} --criteria_for_best_ckpt pc --gpu_id ${GPU_ID} 

# Evaluation
eval_folder=anet_c3d_pdvc # specify the folder to be evaluated
python eval.py --eval_folder ${eval_folder} --eval_transformer_input_type queries --gpu_id ${GPU_ID}
  1. PDVC with ground-truth proposals
# Training
config_path=cfgs/anet_c3d_pdvc_gt.yml
python train.py --cfg_path ${config_path} --criteria_for_best_ckpt pc --gpu_id ${GPU_ID}

# Evaluation
eval_folder=anet_c3d_pdvc_gt
python eval.py --eval_folder ${eval_folder} --eval_transformer_input_type gt_proposals --gpu_id ${GPU_ID}

Performance

Dense video captioning

Model Features config_path Url Recall Precision BLEU4 METEOR2018 METEOR2021 CIDEr SODA_c
PDVC_light C3D cfgs/anet_c3d_pdvcl.yml Google Drive 55.30 58.42 1.55 7.13 7.66 24.80 5.23
PDVC C3D cfgs/anet_c3d_pdvc.yml Google Drive 55.20 57.36 1.82 7.48 8.09 28.16 5.47
PDVC_light TSN cfgs/anet_tsn_pdvcl.yml Google Drive 55.34 57.97 1.66 7.41 7.97 27.23 5.51
PDVC TSN cfgs/anet_tsn_pdvc.yml Google Drive 56.21 57.46 1.92 8.00 8.63 29.00 5.68
PDVC_light TSP cfgs/anet_tsp_pdvcl.yml Google Drive 55.24 57.78 1.77 7.94 8.55 28.25 5.95
PDVC TSP cfgs/anet_tsp_pdvc.yml Google Drive 55.79 57.39 2.17 8.37 9.03 31.14 6.05

Notes:

Video paragraph captioning

Model Features config_path BLEU4 METEOR CIDEr
PDVC C3D cfgs/anet_c3d_pdvc.yml 9.67 14.74 16.43
PDVC TSN cfgs/anet_tsn_pdvc.yml 10.18 15.96 20.66
PDVC TSP cfgs/anet_tsp_pdvc.yml 10.46 16.42 20.91

Notes:

  • Paragraph-level scores are evaluated on the ActivityNet Entity ae-val set.

Citation

If you find this repo helpful, please consider citing:

@inproceedings{wang2021end,
  title={End-to-End Dense Video Captioning with Parallel Decoding},
  author={Wang, Teng and Zhang, Ruimao and Lu, Zhichao and Zheng, Feng and Cheng, Ran and Luo, Ping},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={6847--6857},
  year={2021}
}

@ARTICLE{wang2021echr,
  author={Wang, Teng and Zheng, Huicheng and Yu, Mingjing and Tian, Qian and Hu, Haifeng},
  journal={IEEE Transactions on Circuits and Systems for Video Technology}, 
  title={Event-Centric Hierarchical Representation for Dense Video Captioning}, 
  year={2021},
  volume={31},
  number={5},
  pages={1890-1900},
  doi={10.1109/TCSVT.2020.3014606}}

Acknowledgement

The implementation of Deformable Transformer is mainly based on Deformable DETR. The implementation of the captioning head is based on ImageCaptioning.pytorch. We thanks the authors for their efforts.