VV
ENT-00002038 · Jeu de données

VQA v2

VQA v2 is a dataset associated with VQA Project, classified in LXKeys.world as AI Dataset.

Niveau V PersistantActifDocumenté
Fiche publique

Vue d’ensemble

VQA v2 is a data or evaluation resource used in artificial intelligence research. It is associated with VQA Project and is documented as visual question answering benchmark.

Chronologie

  1. 2017Initial dataset release

    VQA v2 entered the documented public record in 2017. This event is retained at the precision supported by the Entry’s reviewed source history.

Capacités

Evaluation resource for AI systemsDataset or benchmark documentationMachine-learning task referenceSource-linked registry metadataRelationship graph compatibility

Limites connues

Coverage and licensing should be interpreted through the linked source record.Benchmark scores or dataset contents can evolve over time through external updates.
Détail technique et structuré

Description technique

Structured LXKeys.world registry record for VQA v2. Entity type: Dataset; classification: AI Dataset; creator/organization context: Virginia Tech / Georgia Tech. Canonical source anchor: https://visualqa.org. The record tracks source authority, first public appearance, timeline, capabilities, limitations, registry status and graph relationships. Automatic refresh is limited to sources explicitly classified as OFFICIAL and enabled for updates; documentary and research references remain non-authoritative unless reviewed.

Tags contrôlés

DatasetBenchmarkEvaluationData Resource
Graphe relationnel

VQA v2

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VQA v2
CréateurSortante
Virginia Tech / Georgia Tech

Virginia Tech / Georgia Tech is documented as creator, contributor or originating team for VQA v2.

Organisation associéeSortante
VQA Project

VQA Project is the organization or institutional context associated with VQA v2.

ClassificationSortante
Evaluation / Data Resource

VQA v2 is classified as Evaluation / Data Resource.

Couche lisible par machine

Données structurées de l’entrée pour les outils humains, les systèmes IA et les clients machine.

{
    "@context": [
        "https://schema.org",
        {
            "lxw": "https://lxkeys.world/schema/"
        }
    ],
    "@type": "Thing",
    "identifier": "ENT-00002038",
    "name": "VQA v2",
    "alternateName": [],
    "additionalType": {
        "category": "Data and Evaluation",
        "type": "Dataset",
        "subtype": "",
        "lxkeysEntity": false
    },
    "description": "VQA v2 is a dataset associated with VQA Project, classified in LXKeys.world as AI Dataset.",
    "creator": "Virginia Tech / Georgia Tech",
    "url": "https://lxkeys.world/entry.php?id=ENT-00002038&lang=fr",
    "sameAs": "https://visualqa.org",
    "image": "",
    "lxkeysWorld": {
        "worldId": "ENT-00002038",
        "kind": "Data and Evaluation",
        "type": "Dataset",
        "subtype": "",
        "classification": "AI Dataset",
        "organization": "VQA Project",
        "originContext": "AI evaluation and data infrastructure",
        "firstPublicAppearance": "2017",
        "currentStatus": "Active",
        "documentationStatus": "Documented",
        "documentationIndex": {
            "total": 88,
            "documentation": 25,
            "evidence": 13,
            "structure": 25,
            "relationships": 25,
            "level": "Level V Persistent"
        },
        "lxCalendarium": {
            "start_date_utc": "2023-04-01",
            "created_utc": "2026-06-17T01:33:48+00:00",
            "created_dypclt": "D-0 Y-2 P-3 C-3 L-22 T-4",
            "updated_utc": "2026-06-17T01:33:48+00:00",
            "updated_dypclt": "D-0 Y-2 P-3 C-3 L-22 T-4"
        },
        "facts": [],
        "capabilities": [
            "Evaluation resource for AI systems",
            "Dataset or benchmark documentation",
            "Machine-learning task reference",
            "Source-linked registry metadata",
            "Relationship graph compatibility"
        ],
        "limitations": [
            "Coverage and licensing should be interpreted through the linked source record.",
            "Benchmark scores or dataset contents can evolve over time through external updates."
        ],
        "tags": [
            "Dataset",
            "Benchmark",
            "Evaluation",
            "Data Resource"
        ],
        "timeline": [
            {
                "date": "2017",
                "title": "Initial dataset release",
                "description": "VQA v2 entered the documented public record in 2017. This event is retained at the precision supported by the Entry’s reviewed source history.",
                "source_url": "https://visualqa.org",
                "verification_status": "source_backed_official"
            }
        ],
        "relationships": [
            {
                "target": "Virginia Tech / Georgia Tech",
                "type": "Creator",
                "description": "Virginia Tech / Georgia Tech is documented as creator, contributor or originating team for VQA v2.",
                "evidence_level": "documentary"
            },
            {
                "target": "VQA Project",
                "type": "Associated organization",
                "description": "VQA Project is the organization or institutional context associated with VQA v2.",
                "evidence_level": "documentary"
            },
            {
                "target": "Evaluation / Data Resource",
                "type": "Classification",
                "description": "VQA v2 is classified as Evaluation / Data Resource.",
                "evidence_level": "documentary"
            }
        ],
        "sources": [
            {
                "label": "Official documentation",
                "url": "https://visualqa.org",
                "source_type": "Reference Source",
                "verification_status": "verified",
                "authority": "OFFICIAL",
                "role": "primary",
                "update_enabled": true,
                "authority_basis": "curated-corpus-refresh-2026-09-08"
            }
        ],
        "canonical": [],
        "imageMeta": []
    }
}
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