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 576–600 of 2167 papers

Title	Date	Tasks	Status	Hype
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering	Feb 25, 2019	Question AnsweringVisual Question Answering (VQA)	CodeCode Available	1
Faithful Multimodal Explanation for Visual Question Answering	Sep 8, 2018	Explanatory Visual Question AnsweringQuestion Answering	CodeCode Available	1
R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question Answering	May 24, 2018	Question AnsweringRelation	CodeCode Available	1
Compositional Attention Networks for Machine Reasoning	Mar 8, 2018	Referring Expression ComprehensionVisual Question Answering (VQA)	CodeCode Available	1
AI2-THOR: An Interactive 3D Environment for Visual AI	Dec 14, 2017	Deep Reinforcement LearningImitation Learning	CodeCode Available	1
Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments	Nov 20, 2017	Reinforcement LearningTranslation	CodeCode Available	1
FiLM: Visual Reasoning with a General Conditioning Layer	Sep 22, 2017	Image Retrieval with Multi-Modal QueryVisual Question Answering (VQA)	CodeCode Available	1
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering	Jul 25, 2017	Image CaptioningVisual Question Answering	CodeCode Available	1
ParlAI: A Dialog Research Software Platform	May 18, 2017	reinforcement-learningReinforcement Learning	CodeCode Available	1
Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning	Mar 20, 2017	Deep Reinforcement Learningreinforcement-learning	CodeCode Available	1
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning	Dec 20, 2016	DiagnosticQuestion Answering	CodeCode Available	1
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization	Oct 7, 2016	General ClassificationImage Attribution	CodeCode Available	1
Hierarchical Question-Image Co-Attention for Visual Question Answering	May 31, 2016	Visual DialogVisual Question Answering	CodeCode Available	1
Stacked Attention Networks for Image Question Answering	Nov 7, 2015	Visual Question Answering (VQA)	CodeCode Available	1
VQA: Visual Question Answering	May 3, 2015	Image CaptioningMultiple-choice	CodeCode Available	1
VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning	Jul 17, 2025	Language ModelingLanguage Modelling	CodeCode Available	0
MGFFD-VLM: Multi-Granularity Prompt Learning for Face Forgery Detection with VLM	Jul 16, 2025	AttributeFace Swapping	—Unverified	0
Evaluating Attribute Confusion in Fashion Text-to-Image Generation	Jul 9, 2025	Attributecross-modal alignment	—Unverified	0
LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation	Jul 9, 2025	Question AnsweringVisual Question Answering	—Unverified	0
DrishtiKon: Multi-Granular Visual Grounding for Text-Rich Document Images	Jun 26, 2025	document understandingOptical Character Recognition (OCR)	CodeCode Available	0
SMMILE: An Expert-Driven Benchmark for Multimodal Medical In-Context Learning	Jun 26, 2025	In-Context LearningMedical Visual Question Answering	—Unverified	0
Bridging Video Quality Scoring and Justification via Large Multimodal Models	Jun 26, 2025	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified	0
HRIBench: Benchmarking Vision-Language Models for Real-Time Human Perception in Human-Robot Interaction	Jun 25, 2025	BenchmarkingPerson Identification	CodeCode Available	0
FOCUS: Internal MLLM Representations for Efficient Fine-Grained Visual Question Answering	Jun 25, 2025	Question AnsweringVisual Question Answering	—Unverified	0
GEMeX-ThinkVG: Towards Thinking with Visual Grounding in Medical VQA via Reinforcement Learning	Jun 22, 2025	Answer GenerationDecision Making	—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