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README.md

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+---
+task_categories:
+- image-to-text
+viewer: false
+---
+# RAVENEA
+
+[📃 PAPER](https://arxiv.org/abs/2505.14462) | [💻 GITHUB](https://github.com/yfyuan01/RAVENEA)
+
+**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](https://arxiv.org/abs/2505.14462).
+
+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.
+```python
+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.:
+
+```python
+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
+```bibtex
+@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}
+}
+```