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 371–380 of 2167 papers

Title	Date	Tasks	Status	Hype
A Symmetric Dual Encoding Dense Retrieval Framework for Knowledge-Intensive Visual Question Answering	Apr 26, 2023	DecoderKnowledge Distillation	CodeCode Available	1
MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale	Dec 6, 2024	Multimodal ReasoningVisual Question Answering	CodeCode Available	1
MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot Prompting	Oct 13, 2022	Image CaptioningQuestion Answering	CodeCode Available	1
MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training Model	Oct 11, 2022	Contrastive LearningImage-text matching	CodeCode Available	1
FunQA: Towards Surprising Video Comprehension	Jun 26, 2023	Question AnsweringText Generation	CodeCode Available	1
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts	Feb 17, 2021	Caption GenerationDiversity	CodeCode Available	1
MD-VQA: Multi-Dimensional Quality Assessment for UGC Live Videos	Mar 27, 2023	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	1
GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K Resolution	May 27, 2025	8kAvg	CodeCode Available	1
Greedy Gradient Ensemble for Robust Visual Question Answering	Jul 27, 2021	Question AnsweringVisual Question Answering	CodeCode Available	1
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features	Jan 14, 2020	ClassificationDiversity	CodeCode Available	1

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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