--- 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: - 1KACL2026 Findings paper titled "VLURes: Benchmarking Long-Text Grounding and Cross-Lingual Robustness in Vision Language Models." 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** | 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. --- ## 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": , "text": "...", "language": "en" } ``` To inspect the language distribution: ```python from collections import Counter langs = Counter(dataset["train"]["language"]) print(langs) ``` --- ## Intended Uses VLURes is intended for research use in: * 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.