README.md

---
license: cc-by-nc-sa-4.0
language:
- en
- ja
- sw
- ur
multilinguality:
- multilingual
pretty_name: VLURes
tags:
- vision-language
- multimodal
- benchmarking
- low-resource-languages
- cross-lingual-evaluation
- long-text-grounding
- image-text
- acl-2026
size_categories:
- 1K<n<10K
---

# VLURes: Benchmarking Long-Text Grounding and Cross-Lingual Robustness in Vision Language Models

## Dataset Description

**VLURes** is a multilingual benchmark for evaluating the fine-grained visual and linguistic understanding of Vision-Language Models (VLMs) in long-text settings. It was created to move beyond short-caption, English-centric evaluation and instead test image understanding, long-context grounding, and cross-lingual robustness in culturally diverse settings.

This dataset is associated with our <span style="color: blue;">ACL2026 Findings paper titled "VLURes: Benchmarking Long-Text Grounding and Cross-Lingual Robustness in Vision Language Models."</span>

The current Hugging Face release contains the uploaded image-text pairs in a single multilingual split, with each example consisting of a renamed image file, its paired long-form text, and a language identifier.

### Key Features

* **Multilingual and culturally grounded:** The dataset covers **English, Japanese, Swahili, and Urdu**.
* **Long-text grounding:** Each example pairs an image with substantially richer text than standard short-caption benchmarks.
* **Single multilingual release:** The uploaded Hugging Face version is organized as one `train` split, with language identified in the `language` field.
* **Benchmark-oriented design:** The data supports fine-grained evaluation of VLMs across visual, linguistic, and cross-modal tasks.
* **Low-resource language coverage:** The benchmark includes dedicated resources for **Swahili** and **Urdu**, which remain underrepresented in existing vision-language evaluation datasets.

### Supported Tasks

VLURes was designed to support evaluation across eight tasks:

1. **Object Recognition (OR)**
2. **Scene Understanding (SU)**
3. **Relationship Understanding (RU)**
4. **Semantic Segmentation (SS)**
5. **Image Captioning (IC)**
6. **Image-Text Matching (ITM)**
7. **Unrelatedness (U)**
8. **Visual Question Answering (VQA)**

The Hugging Face release provides the core multilingual image-text pairs. Task prompts, evaluation protocols, and benchmark-specific task formulations are described in the paper and accompanying project materials.

---

## Dataset Structure

### Repository Layout

The uploaded dataset follows the structure below:

```text
VLURes_hf_ready/
├── README.md
└── train/
    ├── metadata.parquet
    └── images/
        ├── en/...
        ├── sw/...
        ├── ur/...
        └── jp/...
```

### Data Format

The dataset is packaged in an `ImageFolder`-style format for easy loading with the Hugging Face `datasets` library.

* `train/metadata.parquet` stores the metadata table.
* `train/images/...` contains the actual image files.
* All images in this release are stored as `.jpg` files after preprocessing.

### Data Instances

In the uploaded `metadata.parquet`, each row contains the following fields:

* `id`: a unique example identifier such as `en_000001`
* `file_name`: the relative path to the image file, for example `images/en/en_000001.jpg`
* `text`: the paired text associated with the image
* `language`: the language code for the example

When loaded with the Hugging Face `datasets` library, the dataset exposes the following features:

* `id`
* `image`
* `text`
* `language`

### Language Codes

The current uploaded release uses the following values in the `language` field:

* `en` for English
* `jp` for Japanese
* `sw` for Swahili
* `ur` for Urdu

Note that the metadata uses `jp` in the actual uploaded files for compatibility with the current release structure.

### Splits

This Hugging Face release currently provides a **single split**:

* `train`

This split contains the multilingual image-text pairs used in the benchmark release.

### Data Size

The current uploaded release contains **3,415** examples in total.

| Language | Number of image-text pairs |
|---|---:|
| English (`en`) | 996 |
| Swahili (`sw`) | 1,030 |
| Urdu (`ur`) | 949 |
| Japanese (`jp`) | 440 |
| **Total** | **3,415** |

<span style="color: red;">We have not included lots of Japanese (ja) image-text pairs in this release due to license restrictions imposed by the respective web sources. For en, sw, ur, we have removed some image-text pairs as well.</span>

---

## Dataset Creation

### Curation Rationale

VLURes was created to evaluate VLMs in settings that require more than shallow image-caption matching. The benchmark emphasizes:

1. multilingual understanding,
2. culturally grounded content,
3. long-text visual grounding,
4. robustness beyond English-only evaluation, and
5. fine-grained multimodal reasoning.

### Source Data

The image-text pairs were curated from publicly accessible web sources, including encyclopedia-style pages, news content, and other article-like web documents containing naturally co-occurring images and text.

The benchmark spans a broad range of topics, including:

* animals
* products
* buildings
* locations
* events
* food
* drinks
* hobbies
* works of art
* organizations

### Image-Text Alignment

For each document, candidate images were matched to the article content and filtered to retain representative image-text pairs suitable for benchmark construction. The final release stores the prepared image files together with their corresponding text in a format that is easy to load and use for research.

### Preprocessing for the Hugging Face Release

For this uploaded release:

* image files were converted into a unified `.jpg` format,
* files were renamed into stable identifiers such as `en_000001.jpg`,
* text content was extracted and cleaned from source text files,
* the dataset was organized into a single multilingual `train` split,
* metadata was consolidated into `metadata.parquet`.

---

## Usage

You can load the dataset directly from Hugging Face using the `datasets` library:

```python
from datasets import load_dataset

dataset = load_dataset("atamiles/VLURes")
print(dataset)
print(dataset["train"][0])
```

A typical example will contain:

```python
{
    "id": "en_000001",
    "image": <PIL image>,
    "text": "...",
    "language": "en"
}
```

To inspect the language distribution:

```python
from collections import Counter

langs = Counter(dataset["train"]["language"])
print(langs)
```

---

## Intended Uses

<span style="color: red;">VLURes is intended for research use in:</span>

* multilingual vision-language evaluation,
* long-text visual grounding,
* cross-lingual robustness analysis,
* multimodal benchmarking,
* low-resource language research.

It may also be useful for studying failure modes of VLMs under long-context and multilingual conditions.

## Out-of-Scope Uses

This dataset was not designed for:

* face recognition or identity inference,
* surveillance applications,
* safety-critical deployment without additional validation,
* legal or medical decision-making,
* commercial reuse without checking the rights associated with the underlying source materials.

---

## Important Note on Copyright and Licensing

The benchmark release is shared for research use under the license specified for this repository.

Because the data originates from public web sources, **users are responsible for ensuring that their use of the released materials complies with any applicable third-party rights, copyright restrictions, and terms of use associated with the original source content.**

If you plan to redistribute, adapt, or deploy the contents beyond research use, please verify the status of the original source materials independently.

---

## Citation

If you use VLURes in your work, please cite the associated paper:

```bibtex
@misc{atuhurra2025vluresbenchmarkingvlmvisual,
      title={VLURes: Benchmarking VLM Visual and Linguistic Understanding in Low-Resource Languages}, 
      author={Jesse Atuhurra and Iqra Ali and Tomoya Iwakura and Hidetaka Kamigaito and Tatsuya Hiraoka},
      year={2025},
      eprint={2510.12845},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2510.12845}, 
}
```

We will update the citation block with the final proceedings metadata when it becomes available.

---

## Contact

For questions about the dataset, benchmark, or associated paper, please use the project repository or contact Jesse Atuhurra.