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 101–110 of 2167 papers

Title	Date	Tasks	Status	Hype
SpatialLLM: A Compound 3D-Informed Design towards Spatially-Intelligent Large Multimodal Models	May 1, 2025	Spatial ReasoningVisual Question Answering (VQA)	—Unverified	0
Localizing Before Answering: A Hallucination Evaluation Benchmark for Grounded Medical Multimodal LLMs	Apr 30, 2025	HallucinationHallucination Evaluation	—Unverified	0
VideoMultiAgents: A Multi-Agent Framework for Video Question Answering	Apr 25, 2025	Caption GenerationEgoSchema	CodeCode Available	1
Data-Driven Calibration of Prediction Sets in Large Vision-Language Models Based on Inductive Conformal Prediction	Apr 24, 2025	Conformal PredictionHallucination	—Unverified	0
An Empirical Study on Prompt Compression for Large Language Models	Apr 24, 2025	ArticlesMath	CodeCode Available	3
A Comprehensive Survey of Knowledge-Based Vision Question Answering Systems: The Lifecycle of Knowledge in Visual Reasoning Task	Apr 24, 2025	Question AnsweringRetrieval	—Unverified	0
NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and Results	Apr 17, 2025	FormImage Super-Resolution	CodeCode Available	1
Bridging the Semantic Gaps: Improving Medical VQA Consistency with LLM-Augmented Question Sets	Apr 16, 2025	DiversityMedical Visual Question Answering	—Unverified	0
Instruction-augmented Multimodal Alignment for Image-Text and Element Matching	Apr 16, 2025	Image AugmentationImage Generation	—Unverified	0
DVLTA-VQA: Decoupled Vision-Language Modeling with Text-Guided Adaptation for Blind Video Quality Assessment	Apr 16, 2025	Language ModelingLanguage Modelling	—Unverified	0

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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