multitask-end-to-end-video-captioning: A Python repository from adwardlee

End-to-End Video Captioning with Multitask Reinforcement Learning

This repo holds the codes and models for the end-to-end captioning method presented on WACV 2019

End-to-End Video Captioning with Multitask Reinforcement Learning

Lijun Li, Boqing Gong

[Arxiv Preprint]

If you use our code, please cite our paper.

Prerequisites

This code requires tensorflow1.1.0. The evaluation code is in Python, and you need to install coco-caption evaluation if you want to evaluate the model.

use the following to clone to your local machine

git clone https://github.com/adwardlee/multitask-end-to-end-video-captioning.git

Download Datasets

We support experimenting with two publicly available datasets for video captioning: MSVD & MSR-VTT.

MSVD
MSR-VTT

Preprocess data

Extract all frames from videos

It needs to extract the frames by using cpu_extract.py. Then use read_certrain_number_frame.py to uniformly sample 5 frames from all frames of a video. At last use the tf_feature_extract.py and modify the model path to extract the inception-resnet-v2 features of frame.

Training from scratch

use the *_s2vt.py. Before that, it needs to change the model path of evaluation function and some global parameters in the file. For example,

Step 1

python tf_s2vt.py --gpu 0 --task train

Step 2

Using the pretrained model from step 1 and then

python reinforcement_multisampling_tf_s2vt.py --task train

Step 3

Using the pretrained model from step 2 and then

python reinforce_multitask_e2e_attribute_s2vt.py --task train

Testing existing models

Evaluate models

use the *_s2vt.py. Before that, it needs to change the model path of evaluation function and some global parameters in the file. For example,

python tf_s2vt.py --gpu 0 --task evaluate

for testing the pretrained models, please refer to Repo

The MSVD models can be downloaded from here The MSR-VTT models can be downloaded from here

we also apply temporal attention in tensorflow