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InfographicVQA

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arxiv 2104.12756 v2 pith:4JQTQYBB submitted 2021-04-26 cs.CV cs.CL

Minesh Mathew , Viraj Bagal , Rubèn Pérez Tito , Dimosthenis Karatzas , Ernest Valveny , C.V Jawahar This is my paper · ORCID
classification cs.CVcs.CL
keywords datasetquestionselementsgraphicalinfographicsinfographicvqarequiretextual
verification ladder T0 review T1 audit T2 compute T3 formal
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Infographics are documents designed to effectively communicate information using a combination of textual, graphical and visual elements. In this work, we explore the automatic understanding of infographic images by using Visual Question Answering technique.To this end, we present InfographicVQA, a new dataset that comprises a diverse collection of infographics along with natural language questions and answers annotations. The collected questions require methods to jointly reason over the document layout, textual content, graphical elements, and data visualizations. We curate the dataset with emphasis on questions that require elementary reasoning and basic arithmetic skills. Finally, we evaluate two strong baselines based on state of the art multi-modal VQA models, and establish baseline performance for the new task. The dataset, code and leaderboard will be made available at http://docvqa.org

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Cited by 4 Pith papers

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

  1. IGenBench: Benchmarking the Reliability of Text-to-Infographic Generation

    cs.LG 2026-01 conditional novelty 6.0 of 10

    IGenBench's 600-case, 5,259-question benchmark shows all ten tested text-to-image models are unreliable at end-to-end infographic generation; the best achieves Q-ACC 0.90 but I-ACC 0.49 and data-related checks average...

  2. Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models

    cs.LG 2025-10 conditional novelty 6.0 of 10

    A dynamic replay and reweighting scheduler (RECAP) preserves general capabilities during RLVR while keeping reasoning performance at least as good as reasoning-only finetuning.

  3. Mage-VL: An Efficient Codec-Native Streaming Multimodal Foundation Model

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Codec-guided sparse patch selection plus a lightweight speak/silent gate yields a 4B streaming VLM that is competitive on static tasks, stronger on video/spatial benchmarks, and much cheaper at inference.

  4. Reconstructing Content with Collaborative Attention for Universal Multimodal Representation Learning

    cs.IR 2026-03 conditional novelty 5.0 of 10

    CoCoA forces an MLLM to reconstruct masked text through a single EOS token, improving multimodal embedding quality on MMEB-V1 and matching MoCa at 3B with far less pretraining data.

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