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 851–875 of 2167 papers

Title	Date	Tasks	Status	Score
Few-Shot Multimodal Explanation for Visual Question Answering	Oct 28, 2024	Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)	CodeCode Available	5
Federated Document Visual Question Answering: A Pilot Study	May 10, 2024	Federated LearningQuestion Answering	CodeCode Available	5
Self Supervision for Attention Networks	Jan 6, 2021	image-classificationImage Classification	CodeCode Available	5
Semantically Distributed Robust Optimization for Vision-and-Language Inference	Oct 14, 2021	Data AugmentationNatural Language Inference	CodeCode Available	5
MIRTT: Learning Multimodal Interaction Representations from Trilinear Transformers for Visual Question Answering	Nov 1, 2021	multimodal interactionMultiple-choice	CodeCode Available	5
Factor Graph Attention	Apr 11, 2019	Graph AttentionQuestion Answering	CodeCode Available	5
Active Learning for Visual Question Answering: An Empirical Study	Nov 6, 2017	Active LearningVisual Question Answering	CodeCode Available	5
Incorporating Probing Signals into Multimodal Machine Translation via Visual Question-Answering Pairs	Oct 26, 2023	AttributeMachine Translation	CodeCode Available	5
MHSAN: Multi-Head Self-Attention Network for Visual Semantic Embedding	Jan 11, 2020	Image CaptioningImage-text Retrieval	CodeCode Available	5
D3: Data Diversity Design for Systematic Generalization in Visual Question Answering	Sep 15, 2023	DiversityQuestion Answering	CodeCode Available	5
Inferring and Executing Programs for Visual Reasoning	May 10, 2017	Visual Question Answering (VQA)Visual Reasoning	CodeCode Available	5
Are VLMs Really Blind	Oct 29, 2024	Language ModelingLanguage Modelling	CodeCode Available	5
Mimic and Fool: A Task Agnostic Adversarial Attack	Jun 11, 2019	Adversarial AttackImage Captioning	CodeCode Available	5
Med-PMC: Medical Personalized Multi-modal Consultation with a Proactive Ask-First-Observe-Next Paradigm	Aug 16, 2024	Decision MakingMedical Visual Question Answering	CodeCode Available	5
Exploring the Potential of Encoder-free Architectures in 3D LMMs	Feb 13, 2025	Inductive BiasVisual Question Answering (VQA)	CodeCode Available	5
Exploring the Effectiveness of Video Perceptual Representation in Blind Video Quality Assessment	Jul 8, 2022	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	5
MedHallTune: An Instruction-Tuning Benchmark for Mitigating Medical Hallucination in Vision-Language Models	Feb 28, 2025	Decision MakingHallucination	CodeCode Available	5
Measuring Faithful and Plausible Visual Grounding in VQA	May 24, 2023	Question AnsweringVisual Grounding	CodeCode Available	5
Exploring Modulated Detection Transformer as a Tool for Action Recognition in Videos	Sep 21, 2022	Action DetectionAction Recognition	CodeCode Available	5
InstructOCR: Instruction Boosting Scene Text Spotting	Dec 20, 2024	Optical Character Recognition (OCR)Text Spotting	CodeCode Available	5
Exploring Models and Data for Image Question Answering	May 8, 2015	Image Segmentationobject-detection	CodeCode Available	5
Integrating Image Features with Convolutional Sequence-to-sequence Network for Multilingual Visual Question Answering	Mar 22, 2023	Question AnsweringVisual Question Answering	CodeCode Available	5
Medical Large Vision Language Models with Multi-Image Visual Ability	May 25, 2025	Question AnsweringVisual Question Answering (VQA)	CodeCode Available	5
SparrowVQE: Visual Question Explanation for Course Content Understanding	Nov 12, 2024	Question AnsweringVisual Question Answering	CodeCode Available	5
Are Red Roses Red? Evaluating Consistency of Question-Answering Models	Jul 1, 2019	Question Answeringvalid	CodeCode Available	5

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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