PDVC

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
Introduction
Preparation
Running PDVC on Your Own Videos
Training and Validation
Performance
- Dense video captioning
- Video paragraph captioning
Citation
Acknowledgement

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 result 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.

Preparation

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

Clone the repo

git clone --recursive https://github.com/ttengwang/PDVC.git

Create virtual 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

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.

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

The preprocessed C3D and TSN features have been uploaded to google drive

Dense Video Captioning

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}

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

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}

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 (with learnt proposals)

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:

In the paper, we follow the most previous methods to use the evaluation toolkit in ActivityNet Challenge 2018. Note that the latest evluation tookit (METEOR2021) gives the same CIDEr/BLEU4 but a higher METEOR score.
In the paper, we use an old version of SODA_c implementation, while here we use an updated version for convenience.

Video paragraph captioning (with learnt proposals)

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