task_categories:visual-question-answering size_categories:n<1K language:en license:cc-by-nc-4.0 region:us
---
task_categories:
- visual-question-answering
language:
- en
pretty_name: YTTB-VQA
size_categories:
- n<1K
license: cc-by-nc-4.0
---
# Dataset Card for Dataset Name

## Dataset Description

- **Homepage:** https://gordonhu608.github.io/bliva/
- **Repository:** https://github.com/mlpc-ucsd/BLIVA.git
- **Paper:** 
- **Point of Contact:** w1hu@ucsd.edu

### Dataset Summary

The YTTB-VQA Dataset is a collection of 100 Youtube thumbnail question-answer pairs to evaluate the visual perception abilities of in-text images. It covers 13 
categories, including technology, sports, entertainment, movies, music, food, history, etc. 

### Supported Tasks and Leaderboards

This dataset supports many tasks, including visual question answering, image captioning, etc. 

### License
CC-By-NC-4.0

### Languages

The language of the data is primarily English.

## Dataset Structure

### Data Instances

A data instance in this dataset represents entries from a collection augmented by human-generated questions submitted to BLIVA. The answer is then entered into the answer field.

### Data Fields

**video_id:** a unique string representing a specific YouTube thumbnail image.<br>
**question:** representing a human-generated question.<br>
**video_classes:** representing a specific category for the YouTube thumbnail image.<br>
**answers:** This represents a ground truth answer for the question made about the YouTube thumbnail image.<br>
**video link** Representing the URL link for each YouTube video.

### Data Splits

The data are unsplit.

## Dataset Creation

### Source Data

#### Initial Data Collection and Normalization

We randomly selected YouTube videos with text-rich thumbnails from different categories during the data collection. 
We recorded the unique video ID for each YouTube video and obtained the high-resolution thumbnail from the
URL ”http://img.youtube.com/vi/YouTube-Video-ID/maxresdefault.jpg”. 

### Annotations

#### Annotation process

We created the annotation file with the following fields: ”video id,” question,” video classes,” answers,” and ”video link" in JSON format. 

## Considerations for Using the Data

### Discussion of Biases

Although our dataset spans 13 categories, the ratio within each category varies. For example, 18% of the dataset pertains to sports, while only 3% is dedicated to movies.

### Acknowledgments

The youtube thumbnails dataset is purely for academic research and not for any monetary uses. For any of the authors who saw our dataset and found their thumbnail images used inappropriately, please get in touch with us directly by this email at w1hu@ucsd.edu and we will remove the image immediately.