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Document Visual Question Answering Challenge 2020

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arxiv 2008.08899 v2 pith:FENTL7TC submitted 2020-08-20 cs.CV cs.IR

Minesh Mathew , Ruben Tito , Dimosthenis Karatzas , R. Manmatha , C.V. Jawahar This is my paper · ORCID
classification cs.CVcs.IR
keywords documenttaskchallengeimagesquestionansweringvisualcomprising
verification ladder T0 review T1 audit T2 compute T3 formal

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This paper presents results of Document Visual Question Answering Challenge organized as part of "Text and Documents in the Deep Learning Era" workshop, in CVPR 2020. The challenge introduces a new problem - Visual Question Answering on document images. The challenge comprised two tasks. The first task concerns with asking questions on a single document image. On the other hand, the second task is set as a retrieval task where the question is posed over a collection of images. For the task 1 a new dataset is introduced comprising 50,000 questions-answer(s) pairs defined over 12,767 document images. For task 2 another dataset has been created comprising 20 questions over 14,362 document images which share the same document template.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. InSight-doc: Agentic Visual Perception for Long-Document Understanding

    cs.CV 2026-08 conditional novelty 6.0 of 10

    InSight-doc trains an 8B vision-language model to zoom into document sub-regions on demand, improving long-document VQA accuracy by up to 16.4 points while cutting latency by 41-68%.

  2. DocMIA: Document-Level Membership Inference Attacks against DocVQA Models

    cs.LG 2025-02 conditional novelty 6.0 of 10

    Document-level membership inference for DocVQA models is achieved by measuring parameter fine-tuning distance (and step count) per question-answer pair, in white-box and distilled black-box settings.

  3. Multi-Agent Interactive Question Generation Framework for Long Document Understanding

    cs.CL 2025-07 conditional novelty 4.0 of 10

    A multi-agent question generation pipeline produces long-context English and Arabic QA pairs (AraEngLongBench), and top LVLMs score below 50% on the resulting benchmark.

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