README.md

VIOLIN: A Large-Scale Dataset for Video-and-Language Inference

Data and code for CVPR 2020 paper: "VIOLIN: A Large-Scale Dataset for Video-and-Language Inference"

We introduce a new task, Video-and-Language Inference, for joint multimodal understanding of video and text. Given a video clip with aligned subtitles as premise, paired with a natural language hypothesis based on the video content, a model needs to infer whether the hypothesis is entailed or contradicted by the given video clip.

Also, we present a new large-scale dataset, named Violin (VIdeO-and-Language INference) for this task, which consists of 95,322 video-hypothesis pairs from 15,887 video clips, spanning over 582 hours of video (YouTube and TV shows). In order to address our new multimodal inference task, a model is required to possess sophisticated reasoning skills, from surface-level grounding (e.g., identifying objects and characters in the video) to in-depth commonsense reasoning (e.g., inferring causal relations of events in the video).

News

• 2020.04.29 Baseline code released, and leaderboard will be available soon.
• 2020.04.04 Data features, subtitles and statements released.
• 2020.03.25 Paper released (arXiv).

Violin Dataset

• Data Statistics

source	#episodes	#clips	avg clip len	avg pos. statement len	avg neg. statement len	avg subtitle len
Friends	234	2,676	32.89s	17.94	17.85	72.80
Desperate Housewives	180	3,466	32.56s	17.79	17.81	69.19
How I Met Your Mother	207	1,944	31.64s	18.08	18.06	76.78
Modern Family	210	1,917	32.04s	18.52	18.20	98.50
MovieClips	5,885	5,885	40.00s	17.79	17.81	69.20
All	6,716	15,887	35.20s	18.10	18.04	76.40

• Data Download

  Subtitles and statements (README)

  Image (resnet) features

  C3D features

  Detection features (TODO)

  To obtain raw video data, you can download the source videos yourself (YouTube and TV shows), and then use the span information provided in Subtitles and statements to extract the clips. Also, we might release sampled frames (as images) in the near future.

Baseline Models

• Model Overview

Requirements

• pytorch >= 1.2
• transformers
• h5py
• tqdm
• numpy

Usage

1. Download video features, subtitles and statements and put them into your feat directory.

2. Finetune BERT-base on Violin's training statements, or download our finetuned BERT model.

3. Training

   Using only subtitles

   python main.py --feat_dir [feat dir] --bert_dir [bert dir] --input_streams sub
   

   Using both subtitles and video resnet features (--feat c3d for c3d features)

   python main.py --feat_dir [feat dir] --bert_dir [bert dir] --input_streams sub vid --feat resnet
   

4. Testing

   Testing a specific model

   python main.py --test --feat_dir [feat dir] --bert_dir [bert dir] --input_streams sub vid --feat c3d --model_path [model path]