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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 501–525 of 2167 papers

Title	Date	Tasks	Status	Hype	Score
Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations	Nov 21, 2022	Contrastive LearningRepresentation Learning	CodeCode Available	1	5
3DMIT: 3D Multi-modal Instruction Tuning for Scene Understanding	Jan 6, 2024	Scene UnderstandingVisual Question Answering (VQA)	CodeCode Available	1	5
Evaluating Image Hallucination in Text-to-Image Generation with Question-Answering	Sep 19, 2024	HallucinationHallucination Evaluation	CodeCode Available	1	5
GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection	Nov 5, 2023	Anomaly DetectionQuestion Answering	CodeCode Available	1	5
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections	May 24, 2022	Computational Efficiencycross-modal alignment	CodeCode Available	1	5
Enhancing Vision-Language Pre-Training with Jointly Learned Questioner and Dense Captioner	May 19, 2023	Dense CaptioningImage Captioning	CodeCode Available	1	5
Bridging the Gap between 2D and 3D Visual Question Answering: A Fusion Approach for 3D VQA	Feb 24, 2024	3D Question Answering (3D-QA)Question Answering	CodeCode Available	1	5
Enhancing Visual Question Answering through Question-Driven Image Captions as Prompts	Apr 12, 2024	Image CaptioningQuestion Answering	CodeCode Available	1	5
MapQA: A Dataset for Question Answering on Choropleth Maps	Nov 15, 2022	ArticlesQuestion Answering	CodeCode Available	1	5
MixGen: A New Multi-Modal Data Augmentation	Jun 16, 2022	Data AugmentationImage-text Retrieval	CodeCode Available	1	5
eP-ALM: Efficient Perceptual Augmentation of Language Models	Mar 20, 2023	In-Context LearningVisual Question Answering (VQA)	CodeCode Available	1	5
MixPHM: Redundancy-Aware Parameter-Efficient Tuning for Low-Resource Visual Question Answering	Mar 2, 2023	Mixture-of-ExpertsQuestion Answering	CodeCode Available	1	5
MLP Architectures for Vision-and-Language Modeling: An Empirical Study	Dec 8, 2021	Language ModelingLanguage Modelling	CodeCode Available	1	5
Mining Fine-Grained Image-Text Alignment for Zero-Shot Captioning via Text-Only Training	Jan 4, 2024	DescriptiveImage Captioning	CodeCode Available	1	5
Break It Down: A Question Understanding Benchmark	Jan 31, 2020	Open-Domain Question AnsweringQuestion Answering	CodeCode Available	1	5
MISS: A Generative Pretraining and Finetuning Approach for Med-VQA	Jan 10, 2024	Medical Visual Question AnsweringMulti-Task Learning	CodeCode Available	1	5
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding	Oct 12, 2022	document-image-classificationDocument Image Classification	CodeCode Available	1	5
End-to-end Knowledge Retrieval with Multi-modal Queries	Jun 1, 2023	BenchmarkingCross-Modal Retrieval	CodeCode Available	1	5
MIST: Multi-modal Iterative Spatial-Temporal Transformer for Long-form Video Question Answering	Dec 19, 2022	FormQuestion Answering	CodeCode Available	1	5
MMBERT: Multimodal BERT Pretraining for Improved Medical VQA	Apr 3, 2021	Language ModelingLanguage Modelling	CodeCode Available	1	5
EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Images	Oct 28, 2023	Decision MakingMedical Visual Question Answering	CodeCode Available	1	5
MicroVQA: A Multimodal Reasoning Benchmark for Microscopy-Based Scientific Research	Mar 17, 2025	ArticlesBenchmarking	CodeCode Available	1	5
Efficient Vision-Language Pretraining with Visual Concepts and Hierarchical Alignment	Aug 29, 2022	cross-modal alignmentImage-text Retrieval	CodeCode Available	1	5
Mimic In-Context Learning for Multimodal Tasks	Apr 11, 2025	In-Context LearningVisual Question Answering (VQA)	CodeCode Available	1	5
Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering	Jul 6, 2021	Active LearningObject Recognition	CodeCode Available	1	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	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