VLCN

This repository contains the official code of the paper:

Video Language Co-Attention with Fast-Learning Feature Fusion for VideoQA [PDF]

Adnen Abdessaied, Ekta Sood, Andreas Bulling
Poster
Represnetation Learning for NLP (RepL4NLP) @ ACL 2022 / Dublin, Ireland.

If you find our code useful or use it in your own projects, please cite our paper:

@inproceedings{abdessaied22_repl4NLP,
  author = {Abdessaied, Adnen and Sood, Ekta and Bulling, Andreas},
  title = {Video Language Co-Attention with Multimodal Fast-Learning Feature Fusion for VideoQA},
  booktitle = {Proc. of the 7th Workshop on Representation Learning for NLP (RepL4NLP) @ ACL2022},
  year = {2022},
  pages = {1--12}
}

Overview

Results

Our VLCN model achieves new state-of-the-art results on two open-ended VideoQA datasets MSVD-QA and MSRVTT-QA.

MSVD-QA

Model	What	Who	How	When	Where	All
ST-VQA	18.10	50.00	83.80	72.40	28.60	31.30
Co-Mem	19.60	48.70	81.60	74.10	31.70	31.70
HMEMA	22.40	50.00	73.00	70.70	42.90	33.70
SSML	-	-	-	-	-	35.13
QueST	24.50	52.90	79.10	72.40	50.00	36.10
HCRN	-	-	-	-	-	36.10
MA-DRNN	24.30	51.60	82.00	86.30	26.30	36.20
VLCN (Ours)	28.42	51.29	81.08	74.13	46.43	38.06

MSRVTT-QA

Model	What	Who	How	When	Where	All
ST-VQA	24.50	41.20	78.00	76.50	34.90	30.90
Co-Mem	23.90	42.50	74.10	69.00	42.90	32.00
HMEMA	22.40	50.10	73.00	70.70	42.90	33.70
QueST	27.90	45.60	83.00	75.70	31.60	34.60
SSML	-	-	-	-	-	35.00
HCRN	-	-	-	-	-	35.60
VLCN (Ours)	30.69	44.09	79.82	78.29	36.80	36.01

Requirements

• PyTorch 1.3.1
  
• Torchvision 0.4.2
  
• Python 3.6

Raw data

The raw data of MSVD-QA and MSRVTT-QA are located in data/MSVD-QA and data/MSRVTT-QA , respectively.

Videos: The raw videos of MSVD-QA and MSRVTT-QA can be downloaded from ⬇ and ⬇, respectively.
Text: The text data can be downloaded from ⬇.

After downloading all the raw data, data/MSVD-QA and data/MSRVTT-QA should have the following structure:

Preprocessing

To sample the individual frames and clips and generate the corresponding visual features, we run the script preporocess.py on the raw videos with the appropriate flags. E.g. for MSVD-QA we have to execute

python core/data/preporocess.py --RAW_VID_PATH /data/MSRVD-QA/videos --C3D_PATH path_to_pretrained_c3d

This will save the individual frames and clips in data/MSVD-QA/frames and data/MSVD-QA/clips , respectively, and their visual features in

data/MSVD-QA/frame_feat and data/MSVD-QA/clip_feat, respectively.

Config files

Before starting training, one has to update the config path file cfgs/path_cfgs.py with the paths of the raw data as well as the visual feaures.
All Hyperparameters can be adjusted in cfgs/base_cfgs.py.

Training

To start training, one has to specify an experiment directory EXP_NAME where all the results (log files, checkpoints, tensorboard files etc) will be saved. Futhermore, one needs to specify the MODEL_TYPE of the VLCN to be trained.

MODEL_TYPE	Description
1	VLCN
2	VLCN-FLF
3	VLCV+LSTM
4	MCAN

These parameters can be set inline. E.g. by executing

python run.py --EXP_NAME experiment --MODEL_TYPE 1 --DATA_PATH /data/MSRVD-QA --GPU 1 --SEED 42

Pre-trained models

Our pre-trained models are available here ⬇

Acknowledgements

We thank the Vision and Language Group@ MIL for their MCAN open source implementation, DavidA for his pretrained C3D model and finally ixaxaar for his DNC implementation.

Contributors

• Adnen Abdessaied

For any questions or enquiries, don't not hesitate to contact the above contributor.