Datasets:

sungguk
/

visual-answerability

License sources checked on 2026-09-28. License texts and download URLs are recorded in licenses/; pinned construction resources are recorded in protocol/source-resources.json.

PlotQA: Reasoning over Scientific Plots — Nitesh Methani, Pritha Ganguly, Mitesh M. Khapra, and Pratyush Kumar, WACV 2020. Official dataset/code license statement and pinned source instructions. Dataset: CC BY 4.0. The construction used the pinned Dodon mirror as a transport source, retaining official upstream terms. Charts are re-rendered from source tables; target values, visibility and referents are changed under explicit programs while question text is preserved. The official PlotQA paper describes source data collected from real-world organizations; this package does not assert ownership of those underlying facts or additional third-party rights. Numeric table provenance remains in the per-world metadata.

CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning — Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C. Lawrence Zitnick, and Ross Girshick, CVPR 2017. Official data and license statement. Dataset: CC BY 4.0. Official generator. Scenes are freshly rendered with the official Blender 2.79b/Cycles assets, with source-program attribute changes and object visibility interventions. New label and edit evidence is supplied separately from the upstream data.

GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering — Drew A. Hudson and Christopher D. Manning, CVPR 2019. Official source downloads and official question-generator repository. No explicit annotation redistribution grant was located on these pages or in the downloaded questions1.2.zip and sceneGraphs.zip README files. The public candidate uses source references and local reconstruction. The reconstruction normalizes source JPEGs and applies constant gray patches at source object boxes; symbolic world edits and source-program alternatives support its labels.

Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations — Ranjay Krishna and collaborators, IJCV 2017. Official data page links CC BY 4.0. This is the source of GQA photographs and the basis of its scene annotations. Visual Genome's notice is retained as provenance, not used to infer a license for all GQA-authored annotations. No photographs are bundled in this candidate.

The project software is the Explicit Learning project (2026), Apache 2.0 as declared by its package metadata. The dataset release and documentation are prepared by Sungguk Cha, Mintae Kim, Youngsub Han, Byoung-Ki Jeon, and Sangyeob Lee, using the manuscript author list. No upstream author endorses these edits or the reported model results by virtue of this attribution.