atamiles/VLURes

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@inproceedings{atuhurra-etal-2026-vlures,
    title = "{VLUR}es: Benchmarking Long-Text Grounding and Cross-Lingual Robustness in Vision Language Models",
    author = "Atuhurra, Jesse  and
      Ali, Iqra  and
      Iwakura, Tomoya  and
      Kamigaito, Hidetaka  and
      Hiraoka, Tatsuya",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-acl.1367/",
    pages = "27426--27481",
    ISBN = "979-8-89176-395-1",
    abstract = "We introduce ***VLURes***, a multilingual benchmark for evaluating Vision-Language Models (VLMs) under *long-text grounding*: selecting and reasoning over the image-relevant subset of article-length text that contains distractors and ungrounded claims. *VLURes* contains **4,000** web-curated *image + long-text* pairs across **English (En), Japanese (Ja), Swahili (Sw), and Urdu (Ur)** and **10** topical categories, and defines **eight** tasks spanning image-only perception (OR, SU, RU, SS, IC) and image+text grounding (ITM, *Unrelatedness*, VQA). To construct web-realistic pairs, we apply language-adapted CLIP alignment to select representative images and filter weakly grounded pages. Across **10** pr

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dataset_licence({ "id": "atamiles/VLURes" })
signal: "restrictive"
stated_licence: "cc-by-nc-sa-4.0"
sources: 0