Datasets:

youssef101
/

artelingo

	---
	license: other
	task_categories:
	- text-generation
	- text-classification
	- image-classification
	- image-to-text
	- text-to-image
	language:
	- en
	- ar
	- zh
	tags:
	- art
	- Affective Captioning
	- Emotions
	- Emotion Prediction
	- Image Captioning
	- Multilingual
	- Cultural
	- Diversity
	pretty_name: ArtELingo
	size_categories:
	- 10K<n<100K
	- 100K<n<1M
	- 1M<n<10M
	multilinguality:
	- multilingual
	source_datasets:
	- original
	---
	# Dataset Card for "ArtELingo"

	## Table of Contents
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Dataset Configurations](#dataset-configurations)
	- [Data Fields](#data-fields)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## Dataset Description

	- Homepage: [artelingo.org/](https://www.artelingo.org/)
	- Repository: [More Information Needed](https://github.com/Vision-CAIR/artelingo)
	- Paper: [More Information Needed](https://arxiv.org/abs/2211.10780)
	- Point of Contact: [More Information Needed]([email protected])

	### Dataset Summary

	ArtELingo is a benchmark and dataset introduced in a research paper aimed at promoting work on diversity across languages and cultures.
	It is an extension of ArtEmis, which is a collection of 80,000 artworks from WikiArt with 450,000 emotion labels and English-only captions.
	ArtELingo expands this dataset by adding 790,000 annotations in Arabic and Chinese.
	The purpose of these additional annotations is to evaluate the performance of "cultural-transfer" in AI systems.

	The goal of ArtELingo is to encourage research on multilinguality and culturally-aware AI.
	By including annotations in multiple languages and considering cultural differences,
	the dataset aims to build more human-compatible AI that is sensitive to emotional nuances
	across various cultural contexts. The researchers believe that studying emotions in this
	way is crucial to understanding a significant aspect of human intelligence.

	### Supported Tasks and Leaderboards

	We have two tasks:
	- [Emotion Label Prediction](https://eval.ai/web/challenges/challenge-page/2106/overview)
	- [Affective Image Captioning](https://eval.ai/web/challenges/challenge-page/2104/overview)

	Both challenges have a leaderboard on Eval.ai. Submission deadlines can be viewed from the above links.

	In addition, we are hosting the challenge at the ICCV23 workshop [WECIA](https://iccv23-wecia.github.io/). We have cash prizes for winners.

	### Languages

	We have 3 languages: English, Arabic, and Chinese. For each image, we have at least 5 captions in each language.

	In total we have 80,000 images which are downloaded automatically with the dataset.

	## Dataset Structure

	We show detailed information for all the configurations of the dataset.

	### Dataset Configurations

	We have 4 Configurations:

	#### artelingo

	- Size of downloaded dataset files: 23 GB
	- Splits: \['train', 'test', 'val'\]
	- Number of Samples per splits: \[920K, 94.1K, 46.9K\]
	- Loading Script:
	```python
	from datasets import load_dataset
	dataset = load_dataset(path="youssef101/artelingo", name='artelingo')
	```
	you can also provide a `splits:LIST(str)` parameter to avoid downloading the huge files for all the splits. (especially the train set :))
	```python
	from datasets import load_dataset
	dataset = load_dataset(path="youssef101/artelingo", name='artelingo', splits=['val'])
	```
	Notice that this deems the next dev configuration redundant.

	#### dev

	- Size of downloaded dataset files: 3 GB
	- Splits: \['test', 'val'\]
	- Number of Samples per splits: \[94.1K, 46.9K\]
	- Loading Script:
	```python
	from datasets import load_dataset
	dataset = load_dataset(path="youssef101/artelingo", name='dev')
	```

	#### wecia-emo

	Intended for the [WECIA](https://iccv23-wecia.github.io/) emotion prediction challenge. Instances does not have the emotion or the language attributes.
	- Size of downloaded dataset files: 1.2 GB
	- Splits: \['dev'\]
	- Number of Samples per splits: \[27.9K\]
	- Loading Script:
	```python
	from datasets import load_dataset
	dataset = load_dataset(path="youssef101/artelingo", name='wecia-emo')
	```

	#### wecia-cap

	Intended for the [WECIA](https://iccv23-wecia.github.io/) affective caption generation challenge. Instances does not have the text.
	- Size of downloaded dataset files: 1.2 GB
	- Splits: \['dev'\]
	- Number of Samples per splits: \[16.3K\]
	- Loading Script:
	```python
	from datasets import load_dataset
	dataset = load_dataset(path="youssef101/artelingo", name='wecia-cap')
	```

	### Data Fields
	The data fields are the same among all configs.

	- `uid`: a `int32` feature. A unique identifier for each instance.
	- `image`: a `PIL.Image` feature. The image of the artwork from the wikiart dataset.
	- `art_style`: a `string` feature. The art style of the artwork. Styles are a subset from the [wikiart styles](https://www.wikiart.org/en/paintings-by-style).
	- `painting`: a `string` feature. The name of the painting according to the wikiart dataset.
	- `emotion`: a `string` feature. The emotion associated with the image caption pair.
	- `language`: a `string` feature. The language used to write the caption.
	- `text`: a `string` feature. The affective caption that describes the painting under the context of the selected emotion.

	## Dataset Creation

	### Curation Rationale
	ArtELingo is a benchmark and dataset designed to promote research on diversity
	across languages and cultures. It builds upon ArtEmis, a collection of 80,000
	artworks from WikiArt with 450,000 emotion labels and English-only captions.
	ArtELingo extends this dataset by adding 790,000 annotations in Arabic and
	Chinese, as well as 4,800 annotations in Spanish, allowing for the evaluation
	of "cultural-transfer" performance in AI systems. With many artworks having
	multiple annotations in three languages, the dataset enables the investigation
	of similarities and differences across linguistic and cultural contexts.
	Additionally, ArtELingo explores captioning tasks, demonstrating how diversity
	in annotations can improve the performance of baseline AI models. The hope is
	that ArtELingo will facilitate future research on multilinguality and
	culturally-aware AI. The dataset is publicly available, including standard
	splits and baseline models, to support and ease further research in this area.

	### Source Data

	#### Initial Data Collection and Normalization

	ArtELingo uses images from the [wikiart dataset](https://www.wikiart.org/).
	The images are mainly artworks since they are created with the intention to
	have an emotional impact on the viewer. ArtELingo assumes that WikiArt
	is a representative sample of the cultures of interest. While WikiArt
	is remarkably comprehensive, it has better coverage of the West than other
	regions of the world based on WikiArt’s assignment of artworks to nationalities.

	The data was collected via Amazon Mechanical Turk, where only native speakers
	were allowed to annotate the images. The English, Arabic, and Chinese subsets were
	collected by 6377, 656, and 745 workers respectively. All workers were compensated
	with above minimal wage in each respective country.

	#### Who are the source language producers?

	The data comes from Human annotators who natively speak each respective language.

	## Considerations for Using the Data

	### Social Impact of Dataset

	When using the ArtELingo dataset, researchers and developers must be mindful of
	the potential social impact of the data. Emotions, cultural expressions, and
	artistic representations can be sensitive topics, and AI systems trained on such
	data may have implications on how they perceive and respond to users. It is
	crucial to ensure that the dataset's usage does not perpetuate stereotypes or
	biases related to specific cultures or languages. Ethical considerations should
	be taken into account during the development and deployment of AI models trained
	on ArtELingo to avoid any harmful consequences on individuals or communities.

	### Discussion of Biases

	ArtELingo was filtered against hate speech, racism, and obvious stereotypes.
	However, Like any dataset, ArtELingo may contain inherent biases that could
	influence the performance and behavior of AI systems. These biases could
	arise from various sources, such as cultural differences in emotional
	interpretations, variations in annotator perspectives, or imbalances in
	the distribution of annotations across languages and cultures. Researchers
	should be cautious about potential biases that might impact the dataset's
	outcomes and address them appropriately. Transparently discussing and
	documenting these biases is essential to facilitate a fair understanding of the
	dataset's limitations and potential areas of improvement.


	## Additional Information

	### Dataset Curators

	The corpus was put together by [Youssef Mohamed](https://cemse.kaust.edu.sa/people/person/youssef-s-mohamed),
	[Mohamed Abdelfattah](https://people.epfl.ch/mohamed.abdelfattah/?lang=en),
	[Shyma Alhuwaider](https://cemse.kaust.edu.sa/aanslab/people/person/shyma-y-alhuwaider),
	[Feifan Li](https://www.linkedin.com/in/feifan-li-3280a6249/),
	[Xiangliang Zhang](https://engineering.nd.edu/faculty/xiangliang-zhang/),
	[Kenneth Ward Church](https://www.khoury.northeastern.edu/people/kenneth-church/)
	and [Mohamed Elhoseiny](https://cemse.kaust.edu.sa/people/person/mohamed-elhoseiny).

	### Licensing Information

	Terms of Use: Before we are able to offer you access to the database,
	please agree to the following terms of use. After approval, you (the 'Researcher')
	receive permission to use the ArtELingo database (the 'Database') at King Abdullah
	University of Science and Technology (KAUST). In exchange for being able to join the
	ArtELingo community and receive such permission, Researcher hereby agrees to the
	following terms and conditions: [1.] The Researcher shall use the Database only for
	non-commercial research and educational purposes. [2.] The Universities make no
	representations or warranties regarding the Database, including but not limited to
	warranties of non-infringement or fitness for a particular purpose. [3.] Researcher
	accepts full responsibility for his or her use of the Database and shall defend and
	indemnify the Universities, including their employees, Trustees, officers and agents,
	against any and all claims arising from Researcher's use of the Database, and
	Researcher's use of any copies of copyrighted 2D artworks originally uploaded to
	http://www.wikiart.org that the Researcher may use in connection with the Database.
	[4.] Researcher may provide research associates and colleagues with access to the
	Database provided that they first agree to be bound by these terms and conditions.
	[5.] The Universities reserve the right to terminate Researcher's access to the Database
	at any time. [6.] If Researcher is employed by a for-profit, commercial entity,
	Researcher's employer shall also be bound by these terms and conditions, and Researcher
	hereby represents that he or she is fully authorized to enter into this agreement on
	behalf of such employer. [7.] The international copyright laws shall apply to all
	disputes under this agreement.

	### Citation Information

	```
	@inproceedings{mohamed2022artelingo,
	title={ArtELingo: A Million Emotion Annotations of WikiArt with Emphasis on Diversity over Language and Culture},
	author={Mohamed, Youssef and Abdelfattah, Mohamed and Alhuwaider, Shyma and Li, Feifan and Zhang, Xiangliang and Church, Kenneth and Elhoseiny, Mohamed},
	booktitle={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
	pages={8770--8785},
	year={2022}
	}
	```

	### Contributions

	Thanks to [@youssef101](https://github.com/Mo-youssef) for adding this dataset. [@Faizan](https://faixan-khan.github.io/) for testing.