| --- |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: |
| - "MATVB_train.json" |
| - "MATVB_query_nl.json" |
| - "MATVB_query_caption.json" |
| task_categories: |
| - image-to-text |
| - image-retrieval |
| language: |
| - en |
| tags: |
| - image-captioning |
| - image-text-retrieval |
| - mathematics |
| - multimodal |
| - benchmark |
| - wikipedia |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # MAT-VB: MAthematical Text-Vision Benchmark |
|
|
| 🏆 **Best Resource Paper Award** — *2025 ACM/IEEE Joint Conference on Digital Libraries (JCDL)* |
|
|
| Official Hugging Face repository for **MAT-VB**, a multimodal benchmark constructed from Wikipedia mathematical content designed to evaluate mathematical image captioning and text-to-image retrieval models. |
|
|
| --- |
|
|
| ## 🔗 Links |
|
|
| * **GitHub Repository:** [AIIRLab/MATVB](https://github.com/AIIRLab/MATVB) |
| * **Paper (IEEE Xplore):** [MAT-VB: MAthematical Text-Vision Benchmark](https://ieeexplore.ieee.org/abstract/document/11363711) |
|
|
| --- |
|
|
| ## 📁 Dataset Files Overview |
|
|
| | File Name | Format | Description | |
| | :--- | :--- | :--- | |
| | `images.zip` | Archive (`.zip`) | Contains all ~4,000 Wikipedia math images (216 MB uncompressed). | |
| | `MATVB_train.json` | JSON | Training annotations. Maps `image_id` (key) to its ground-truth `caption` (value). | |
| | `MATVB_query_nl.json` | JSON | Natural language query set for text-to-image search (human-authored intent queries). | |
| | `MATVB_query_caption.json` | JSON | Caption query set for text-to-image search (original Wikipedia captions). | |
| | `MATVB_qrel.qrel` | TREC format | Evaluation relevance file mapping query IDs to target image IDs. | |
|
|
| --- |
|
|
| ## 🎯 Task Definitions & Splits |
|
|
| ### 1. Mathematical Image Captioning |
| * **Training Set:** Use all entries in `MATVB_train.json` (3,600 image-caption pairs). |
| * **Test Set:** Any image in `images.zip` whose ID is **not** present in `MATVB_train.json` serves as a test sample (400 test images). Models generate captions for these test images during evaluation. |
|
|
| ### 2. Text-to-Image Retrieval |
| The benchmark provides 400 test evaluation queries under two modalities sharing identical query IDs: |
| * **Natural Language Queries (`MATVB_query_nl.json`):** Human-authored search queries representing intent-based user searches. |
| * **Caption Queries (`MATVB_query_caption.json`):** Queries built using original, detailed Wikipedia captions. |
| * **Ground Truth (`MATVB_qrel.qrel`):** Use standard TREC evaluation tools (e.g., `trec_eval`) to score retrieval runs against this relevance file. |
| |
| --- |
| |
| ## 🚀 How to Load and Use |
| |
| You can download and parse the dataset files directly using `huggingface_hub` and standard Python libraries: |
| |
| ```python |
| import json |
| import zipfile |
| from huggingface_hub import hf_hub_download |
| |
| # 1. Download dataset files |
| repo_id = "AIIRLab/MATVB" |
| |
| train_path = hf_hub_download(repo_id=repo_id, filename="MATVB_train.json", repo_type="dataset") |
| query_nl_path = hf_hub_download(repo_id=repo_id, filename="MATVB_query_nl.json", repo_type="dataset") |
| query_cap_path = hf_hub_download(repo_id=repo_id, filename="MATVB_query_caption.json", repo_type="dataset") |
| qrel_path = hf_hub_download(repo_id=repo_id, filename="MATVB_qrel.qrel", repo_type="dataset") |
| images_zip_path = hf_hub_download(repo_id=repo_id, filename="images.zip", repo_type="dataset") |
| |
| # 2. Extract image archive |
| with zipfile.ZipFile(images_zip_path, 'r') as zip_ref: |
| zip_ref.extractall("./images") |
| |
| # 3. Load JSON annotations |
| with open(train_path, "r") as f: |
| train_data = json.load(f) # dict: {image_id: caption} |
| |
| with open(query_nl_path, "r") as f: |
| queries_nl = json.load(f) # dict: {query_id: query_text} |
| |
| with open(query_cap_path, "r") as f: |
| queries_caption = json.load(f) # dict: {query_id: query_text} |
| |
| print(f"Loaded {len(train_data)} training samples.") |
| print(f"Loaded {len(queries_nl)} NL retrieval queries.") |
