PyTorch code accompanies our EMNLP 2018 paper:

TVQA: Localized, Compositional Video Question Answering

Jie Lei, Licheng Yu, Mohit Bansal, Tamara L. Berg

• Data: TVQA dataset
• Website: http://tvqa.cs.unc.edu
• Submission/Leaderboard: TVQA Leaderboard
• Related works: TVR (Moment Retrieval), TVC (Video Captioning), TVQA+ (Grounded VideoQA)

TVQA is a large-scale video QA dataset based on 6 popular TV shows (Friends, The Big Bang Theory, How I Met Your Mother, House M.D., Grey's Anatomy, Castle). It consists of 152.5K QA pairs from 21.8K video clips, spanning over 460 hours of video. The questions are designed to be compositional, requiring systems to jointly localize relevant moments within a clip, comprehend subtitles-based dialogue, and recognize relevant visual concepts.

• QA example

  

  See examples in video: click here

• Statistics

  TV Show	Genre	#Season	#Episode	#Clip	#QA
  The Big Bang Theory	sitcom	10	220	4,198	29,384
  Friends	sitcom	10	226	5,337	37,357
  How I Met Your Mother	sitcom	5	72	1,512	10,584
  Grey's Anatomy	medical	3	58	1,472	9,989
  House M.D.	medical	8	176	4,621	32,345
  Castle	crime	8	173	4,698	32,886
  Total	-	44	925	21,793	152,545

A multi-stream model, each stream process different contextual inputs.

• Python 2.7
• PyTorch 0.4.0
• tensorboardX
• pysrt
• tqdm
• h5py
• numpy

• ImageNet feature: Extracted from ResNet101, Google Drive link
• Regional Visual Feature: object-level encodings from object detector (too large to share ...)
• Visual Concepts Feature: object labels and attributes from object detector download link. This file is included in download.sh.

For object detector, we used Faster R-CNN trained on Visual Genome, please refer to this repo.

0. Clone this repo

   git clone https://github.com/jayleicn/TVQA.git
   

1. Download data

   Questions, answers and subtitles, etc. can be directly downloaded by executing the following command:

   bash download.sh
   

   For video frames and video features, please visit TVQA Dwonload Page.

2. Preprocess data

   python preprocessing.py
   

   This step will process subtitle files and tokenize all textual sentence.

3. Build word vocabulary, extract relevant GloVe vectors

   For words that do not exist in GloVe, random vectors np.random.randn(self.embedding_dim) * 0.4 are used. 0.4 is the standard deviation of the GloVe vectors

   mkdir cache
   python tvqa_dataset.py
   

4. Training

   python main.py --input_streams sub
   

5. Inference

   python test.py --model_dir [results_dir] --mode valid
   

Please note this is a better version of the original implementation we used for EMNLP paper. Bascially, I rewrote some of the data preprocessing code and updated the model to the latest version of PyTorch, etc. By using this code, you should be able to get slightly higher accuracy (~1%) than our paper.

@inproceedings{lei2018tvqa,
  title={TVQA: Localized, Compositional Video Question Answering},
  author={Lei, Jie and Yu, Licheng and Bansal, Mohit and Berg, Tamara L},
  booktitle={EMNLP},
  year={2018}
}

1. Add data preprocessing scripts
2. Add baseline scripts
3. Add model and training scripts
4. Add test scripts

• Dataset: faq-tvqa-unc [at] googlegroups.com
• Model: Jie Lei, jielei [at] cs.unc.edu