Visual Question Answering (VQA)

Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language.

Image Source: visualqa.org

Papers

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Showing 921–930 of 2167 papers

Title	Date	Tasks	Status
Generating Rationales in Visual Question Answering	Apr 4, 2020	Question AnsweringVisual Question Answering	—Unverified
KAT: A Knowledge Augmented Transformer for Vision-and-Language	Jan 16, 2022	Answer GenerationDecoder	—Unverified
Assessing Visual Quality of Omnidirectional Videos	Jul 14, 2019	Visual Question Answering (VQA)	—Unverified
DePlot: One-shot visual language reasoning by plot-to-table translation	Dec 20, 2022	Chart Question AnsweringFactual Inconsistency Detection in Chart Captioning	—Unverified
Ontology-based knowledge representation for bone disease diagnosis: a foundation for safe and sustainable medical artificial intelligence systems	Jun 5, 2025	DiagnosticMultimodal Deep Learning	—Unverified
Knowing Where to Look? Analysis on Attention of Visual Question Answering System	Oct 9, 2018	Question AnsweringVisual Question Answering	—Unverified
Generating Natural Questions from Images for Multimodal Assistants	Nov 17, 2020	Common Sense ReasoningNatural Questions	—Unverified
Generating Natural Language Explanations for Visual Question Answering using Scene Graphs and Visual Attention	Feb 15, 2019	Explanation GenerationLanguage Modeling	—Unverified
Knowledge-Based Counterfactual Queries for Visual Question Answering	Mar 5, 2023	counterfactualDecision Making	—Unverified
COIN: Counterfactual Image Generation for VQA Interpretation	Jan 10, 2022	counterfactualImage Generation	—Unverified

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All datasets GQA Test2019 VQA v2 test-dev VQA v2 test-std OK-VQA MSVD-QA DocVQA test MSRVTT-QA InfographicVQA GQA test-dev VizWiz 2020 VQA A-OKVQA CLEVR

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	human	Accuracy	89.3	—	Unverified
2	DREAM+Unicoder-VL (MSRA)	Accuracy	76.04	—	Unverified
3	TRRNet (Ensemble)	Accuracy	74.03	—	Unverified
4	MIL-nbgao	Accuracy	73.81	—	Unverified
5	Kakao Brain	Accuracy	73.33	—	Unverified
6	Coarse-to-Fine Reasoning, Single Model	Accuracy	72.14	—	Unverified
7	270	Accuracy	70.23	—	Unverified
8	NSM ensemble (updated)	Accuracy	67.55	—	Unverified
9	VinVL-DPT	Accuracy	64.92	—	Unverified
10	VinVL+L	Accuracy	64.85	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PaLI	Accuracy	84.3	—	Unverified
2	BEiT-3	Accuracy	84.19	—	Unverified
3	VLMo	Accuracy	82.78	—	Unverified
4	ONE-PEACE	Accuracy	82.6	—	Unverified
5	mPLUG (Huge)	Accuracy	82.43	—	Unverified
6	CuMo-7B	Accuracy	82.2	—	Unverified
7	X2-VLM (large)	Accuracy	81.9	—	Unverified
8	MMU	Accuracy	81.26	—	Unverified
9	Lyrics	Accuracy	81.2	—	Unverified
10	InternVL-C	Accuracy	81.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BEiT-3	overall	84.03	—	Unverified
2	mPLUG-Huge	overall	83.62	—	Unverified
3	ONE-PEACE	overall	82.52	—	Unverified
4	X2-VLM (large)	overall	81.8	—	Unverified
5	VLMo	overall	81.3	—	Unverified
6	SimVLM	overall	80.34	—	Unverified
7	X2-VLM (base)	overall	80.2	—	Unverified
8	VAST	overall	80.19	—	Unverified
9	VALOR	overall	78.62	—	Unverified
10	Prompt Tuning	overall	78.53	—	Unverified