/CondensedMovies

Story-Based Retrieval with Contextual Embeddings. Largest freely available movie video dataset. [ACCV'20]

Primary LanguagePython

CondensedMovies

**** N.B: Please use the condensed movies challenge https://github.com/m-bain/CondensedMovies-chall with updated splits since some videos in the original paper are unavailable with missing features ****_

Contact me directly for the additional dataset queries, details in the challenge repo for feature download.

###############################################

This repository contains the video dataset, implementation and baselines from Condensed Movies: Story Based Retrieval with Contextual Embeddings.

Project page | arXiv preprint | Read the paper | Preview the data

CondensedMovies Dataset

videocaptions

The dataset consists of 3K+ movies, 30K+ professionally captioned clips, 1K+ video hours, 400K+ facetracks & precomputed features from 6 different modalities.

Installation

Requirements:

  • Storage
    • 20GB for features (required for baseline experiments)
    • 10GB for facetracks
    • 250GB for videos
  • Libraries
    • ffmpeg (for video download)
    • youtube-dl (for video download)
    • pandas, numpy
    • python 3.6+

Prepare Data

  1. Navigate to directory cd CondensedMovies/data_prep/
  2. Edit configuration file config.json to download desired subsets of the dataset and their destination.
  3. If downloading the source videos (src: true), you can edit youtube-dl.conf for desired resolution, subtitles etc. Please see youtube-dl for more info
  4. Run python download.py

If you have trouble downloading the source videos or features (due to geographical restrictions or otherwise), please contact me.

Video-Text Retrieval

Baseline (Mixture of Expert Embeddings)

Edit data_dir and save_dir in configs/moe.json for the experiments.

  1. python train.py configs/moe.json
  2. python test.py --resume $SAVED_EXP_DIR/model_best.pth

Visualisation

Run python visualise_face_tracks.py with the appropriate arguments to visualise face tracks for a given videoID (requires facetracks and source videos downloaded).

TODO:

  • youtube download script
  • missing videos check
  • precomputed features download script
  • facetrack visualisation
  • dataloader
  • video-text retrieval baselines
  • intra-movie baselines + char module
  • release fixed_seg features

FAQ

Why did some of the source videos fail to download?

This is most likely due to geographical restrictions on the videos, email me at maxbain@robots.ox.ac.uk and I can help.

The precomputed features are averaged over the temporal dimension, will you release the original features?

This is to save space, original features in total are ~1TB, contact me to arrange download of this.

I think clip X is incorrectly identified as being from movie Y, what do do?

Please let me know any movie identification mistakes and I'll correct it ASAP.

Acknowledgements

We would like to thank Samuel Albanie for his help with feature extraction.