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 681–690 of 2167 papers

Title	Date	Tasks	Status
Bridging the Semantic Gaps: Improving Medical VQA Consistency with LLM-Augmented Question Sets	Apr 16, 2025	DiversityMedical Visual Question Answering	—Unverified
DVLTA-VQA: Decoupled Vision-Language Modeling with Text-Guided Adaptation for Blind Video Quality Assessment	Apr 16, 2025	Language ModelingLanguage Modelling	—Unverified
Instruction-augmented Multimodal Alignment for Image-Text and Element Matching	Apr 16, 2025	Image AugmentationImage Generation	—Unverified
PuzzleBench: A Fully Dynamic Evaluation Framework for Large Multimodal Models on Puzzle Solving	Apr 15, 2025	Logical ReasoningVisual Question Answering (VQA)	—Unverified
QAVA: Query-Agnostic Visual Attack to Large Vision-Language Models	Apr 15, 2025	Question AnsweringVisual Question Answering	CodeCode Available
MMKB-RAG: A Multi-Modal Knowledge-Based Retrieval-Augmented Generation Framework	Apr 14, 2025	Question AnsweringRAG	—Unverified
Building Trustworthy Multimodal AI: A Review of Fairness, Transparency, and Ethics in Vision-Language Tasks	Apr 14, 2025	EthicsFairness	—Unverified
PathVLM-R1: A Reinforcement Learning-Driven Reasoning Model for Pathology Visual-Language Tasks	Apr 12, 2025	Computed Tomography (CT)Question Answering	—Unverified
FVQ: A Large-Scale Dataset and A LMM-based Method for Face Video Quality Assessment	Apr 12, 2025	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available
NoTeS-Bank: Benchmarking Neural Transcription and Search for Scientific Notes Understanding	Apr 12, 2025	BenchmarkingDocument AI	—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