RAVENEA

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RAVENEA is a multimodal benchmark designed to comprehensively evaluate the capabilities of VLMs in cultural understanding through RAG, introduced in RAVENEA: A Benchmark for Multimodal Retrieval-Augmented Visual Culture Understanding.

It provides:

• A large-scale cultural retrieval-generation corpus featuring 1,868 culturally grounded images paired with over 10,000 human-ranked Wikipedia documents.

• Two downstream tasks for assessing culture-centric visual understanding (cVQA) and culture-informed image captioning (cIC).

• Broad cross-cultural coverage spanning 8 countries and 11 categories, including China, India, Indonesia, Korea, Mexico, Nigeria, Russia, and Spain. The benchmark encompasses a diverse taxonomic spectrum: Architecture, Cuisine, History, Art, Daily Life, Companies, Sports & Recreation, Transportation, Religion, Nature, and Tools.

Dataset Structure

The dataset is organized as follows:

ravenea/
├── images/                  # Directory containing all images
├── metadata_train.jsonl     # Training split metadata
├── metadata_val.jsonl       # Validation split metadata
├── metadata_test.jsonl      # Test split metadata
├── metadata.jsonl           # Full metadata
├── cic_downstream.jsonl     # culture-informed image captioning task
├── cvqa_downstream.jsonl    # culture-centric visual question answering task
└── wiki_documents.jsonl     # Corpus of Wikipedia articles for retrieval

Schema

Metadata (metadata_*.jsonl)

Each line is a JSON object representing a data sample:

• file_name: Path to the image file (e.g., ./ravenea/images/ccub_101_China_38.jpg).
• country: Country of origin for the cultural content.
• task_type: Task category (e.g., cIC for image captioning/QA).
• category: Broad cultural category (e.g., Daily Life).
• human_captions: Human-written caption describing the image.
• questions: List of questions associated with the image.
• options: Multiple-choice options for the questions.
• answers: Correct answers for the questions.
• enwiki_ids: List of relevant Wikipedia article IDs.
• culture_relevance: Score or indicator of cultural relevance.

Wikipedia Corpus (wiki_documents.jsonl)

Contains the knowledge base for retrieval:

• id: Unique identifier for the article (e.g., enwiki/65457597).
• text: Full text content of the Wikipedia article.
• date_modified: Last modification date of the article.

Usage

Download the Dataset

Please download the dataset then unzip it to the current directory.

from huggingface_hub import hf_hub_download

local_path = hf_hub_download(
    repo_id="jaagli/ravenea",
    filename="./ravenea.zip",
    repo_type="dataset",
    local_dir="./",
)
print(f"File downloaded to: {local_path}")

Loading the Data

You can load the dataset using standard Python libraries.:

import json
from pathlib import Path

def load_jsonl(file_path):
    data = []
    with open(file_path, 'r', encoding='utf-8') as f:
        for line in f:
            data.append(json.loads(line))
    return data

# Load metadata
train_data = load_jsonl("./ravenea/metadata_train.jsonl")
test_data = load_jsonl("./ravenea/metadata_test.jsonl")

# Load Wikipedia corpus
wiki_docs = load_jsonl("./ravenea/wiki_documents.jsonl")
doc_id_to_text = {doc['id']: doc['text'] for doc in wiki_docs}

# Example: Accessing a sample
sample = train_data[0]
print(f"Image: {sample['file_name']}")
print(f"Caption: {sample['human_captions']}")
print(f"Docs: {sample['enwiki_ids']}")

BibTeX Citation

@inproceedings{
li2026ravenea,
title={{RAVENEA}: A Benchmark for Multimodal Retrieval-Augmented Visual Culture Understanding},
author={Jiaang Li and Yifei Yuan and Wenyan Li and Mohammad Aliannejadi and Daniel Hershcovich and Anders S{\o}gaard and Ivan Vuli{\'c} and Wenxuan Zhang and Paul Pu Liang and Yang Deng and Serge Belongie},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=4zAbkxQ23i}
}