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 1131–1140 of 2167 papers

Title	Date	Tasks	Status
ZALM3: Zero-Shot Enhancement of Vision-Language Alignment via In-Context Information in Multi-Turn Multimodal Medical Dialogue	Sep 26, 2024	Medical Visual Question AnsweringQuestion Answering	—Unverified
Zero-Shot Anomaly Detection in Battery Thermal Images Using Visual Question Answering with Prior Knowledge	May 22, 2025	Anomaly DetectionQuestion Answering	—Unverified
Zero-Shot Transfer VQA Dataset	Nov 2, 2018	Question AnsweringTransfer Learning	—Unverified
Zero-Shot Video Question Answering with Procedural Programs	Dec 1, 2023	Code GenerationLanguage Modeling	—Unverified
Zero-Shot Visual Question Answering	Nov 17, 2016	Question AnsweringRetrieval	—Unverified
Zero-Shot Visual Reasoning by Vision-Language Models: Benchmarking and Analysis	Aug 27, 2024	BenchmarkingLarge Language Model	—Unverified
Bidirectional Contrastive Split Learning for Visual Question Answering	Aug 24, 2022	Adversarial AttackBackdoor Attack	—Unverified
Generating Question Relevant Captions to Aid Visual Question Answering	Jun 3, 2019	General KnowledgeImage Captioning	—Unverified
Explainable High-order Visual Question Reasoning: A New Benchmark and Knowledge-routed Network	Sep 23, 2019	Question AnsweringTriplet	—Unverified
Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?	Dec 27, 2021	ArticlesMedical Visual Question Answering	—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	InternVL-C	Accuracy	81.2	—	Unverified
10	Lyrics	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