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 831–840 of 2167 papers

Title	Date	Tasks	Status	Hype
NTIRE 2023 Quality Assessment of Video Enhancement Challenge	Jul 19, 2023	DeblurringImage Restoration	—Unverified	0
A reinforcement learning approach for VQA validation: an application to diabetic macular edema grading	Jul 19, 2023	Medical Image AnalysisQuestion Answering	—Unverified	0
Explaining Autonomous Driving Actions with Visual Question Answering	Jul 19, 2023	Autonomous DrivingAutonomous Vehicles	CodeCode Available	1
Generative Visual Question Answering	Jul 18, 2023	Generative Visual Question AnsweringQuestion Answering	—Unverified	0
Let's ViCE! Mimicking Human Cognitive Behavior in Image Generation Evaluation	Jul 18, 2023	Image GenerationQuestion Answering	—Unverified	0
Towards a performance analysis on pre-trained Visual Question Answering models for autonomous driving	Jul 18, 2023	Autonomous DrivingModel Selection	CodeCode Available	0
Emu: Generative Pretraining in Multimodality	Jul 11, 2023	Image CaptioningImage Generation	CodeCode Available	3
CAT-ViL: Co-Attention Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic Surgery	Jul 11, 2023	Question AnsweringScene Understanding	CodeCode Available	1
Rad-ReStruct: A Novel VQA Benchmark and Method for Structured Radiology Reporting	Jul 11, 2023	Medical Visual Question AnsweringQuestion Answering	CodeCode Available	1
Subjective and Objective Audio-Visual Quality Assessment for User Generated Content	Jul 10, 2023	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	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