FIBER data and code

This repo contains the data and code for the ACL 2022 paper FIBER: Fill-in-the-Blanks as a Challenging Video Understanding Evaluation Framework.

Download the data

• train
• validation
• test

The video features are available in VATEX download page. You should download the train, validation, and public test I3D video features and extract them in some folder (e.g., data/I3D_video_features).

Setup

With Conda installed (or preferably mamba for more speed), run:

conda env create
conda activate lqam
spacy download en_core_web_trf

NB: the scripts mentioned in the rest of this README generally accept many options. Try using the --help (or -h) option or looking at their code for more information.

Dataset Blank Generation

Follow these steps if you wanna re-generate the blanks' dataset. Note you probably don't have to run this.

1. Generate the blanked captions, for each VATEX split JSON file (replacing the variables with values):

   ./scripts/generate_dataset_from_vatex.py $VATEX_JSON_FILE_OR_URL > $GENERATED_JSON_FILE
   

2. Create a list of the available videos (you first need to set the env var GOOGLE_API_KEY that can use the YouTube API):

   jq --raw-output '.[] | .video_id' $GENERATED_JSON_FILE \
     | sed 1d \
     | sort \
     | uniq \
     | ./scripts/list_available_videos.py > $AVAILABLE_VIDEO_IDS_FILE
   

3. Filter the JSON file based on the available videos:

   jq \
     --compact-output \
     --slurpfile ids <((echo '[' && sed 's/.*/"&"/g' < $AVAILABLE_VIDEO_IDS_FILE | paste -s -d, - && echo ']') | jq .) \
     '[ JOIN(INDEX($ids[][]; .); .video_id)[] | select(.[1] != null) | .[0] ]' \
     $GENERATED_JSON_FILE > $GENERATED_JSON_FILTERED_FILE
   

Data Annotation

See Annotation. Note you probably don't need to do this.

Download the videos

In case you want to download the video (which you likely won't because there are video features already available), given a file with one YouTube video ID per line (such as $AVAILABLE_VIDEO_IDS_FILE):

youtube-dl -f "best[ext=mp4]/best" -o "videos/%(id)s.%(ext)s" --batch-file FILE

Training

./scripts/run_model.py --train

Evaluation

Evaluate the T5 text-only baseline:

./scripts/run_model.py

Citation

@inproceedings{castro-etal-2022-fill,
    title = "{FIBER}: Fill-in-the-Blanks as a Challenging Video Understanding Evaluation Framework",
    author = "Castro, Santiago  and
      Wang, Ruoyao  and
      Huang, Pingxuan  and
      Stewart, Ian  and
      Ignat, Oana  and
      Liu, Nan  and
      Stroud, Jonathan C.  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-long.209/",
    pages = "2925--2940"
}

Analyses from the paper

Some analyses from the paper can be found under notebooks/, scripts/, and in this Google Colab.