VQAv2
BenchmarkFreeVisual Question Answering with real images and human questions
Capabilities1 decomposed
multimodal question-answering evaluation
Medium confidenceVQAv2 serves as a benchmark for evaluating vision-language models by providing a dataset of 1.1 million questions paired with 650,000 images from the COCO dataset. It requires models to understand both visual content and generate natural language answers, utilizing a diverse set of question types such as color identification and quantity assessment. This dual requirement distinguishes it from other benchmarks that may focus solely on either vision or language tasks.
VQAv2 combines a large-scale dataset with a diverse range of question types, enabling comprehensive evaluation of vision-language models, unlike simpler datasets that may focus on a narrower scope.
More comprehensive than other visual question-answering benchmarks due to its extensive question variety and large image corpus.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with VQAv2, ranked by overlap. Discovered automatically through the match graph.
MMMU
Expert-level multimodal understanding across 30 subjects.
Tutorial on MultiModal Machine Learning (ICML 2023) - Carnegie Mellon University
11-777: MultiModal Machine Learning (Fall 2022) - Carnegie Mellon University
11-877: Advanced Topics in MultiModal Machine Learning (Fall 2022) - Carnegie Mellon University
MMMU
Massive multitask multimodal understanding (images + text)
Language Is Not All You Need: Aligning Perception with Language Models (Kosmos-1)
* ⭐ 03/2023: [PaLM-E: An Embodied Multimodal Language Model (PaLM-E)](https://arxiv.org/abs/2303.03378)
Best For
- ✓researchers developing and testing vision-language models
Known Limitations
- ⚠Limited to questions that can be answered based on the provided images; may not cover all visual contexts.
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
About
VQAv2 contains 1.1M questions about 650K images from COCO. Questions are varied: 'What color is...?', 'How many...?', etc. Requires both vision (understanding image content) and language (generating answers). Standard for evaluating vision-language models.
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