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 1251–1260 of 2167 papers

Title	Date	Tasks	Status
Attention Mechanism based Cognition-level Scene Understanding	Apr 17, 2022	Question AnsweringScene Understanding	—Unverified
Attention Overlap Is Responsible for The Entity Missing Problem in Text-to-image Diffusion Models!	Oct 28, 2024	DenoisingQuestion Answering	—Unverified
Attentive Explanations: Justifying Decisions and Pointing to the Evidence	Dec 14, 2016	Decision MakingQuestion Answering	—Unverified
Attentive Explanations: Justifying Decisions and Pointing to the Evidence (Extended Abstract)	Nov 17, 2017	Question AnsweringVisual Question Answering (VQA)	—Unverified
Audio-Visual Quality Assessment for User Generated Content: Database and Method	Mar 4, 2023	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified
Augmenting Image Question Answering Dataset by Exploiting Image Captions	May 1, 2018	Data AugmentationImage Captioning	—Unverified
A Unified Framework for Multilingual and Code-Mixed Visual Question Answering	Dec 1, 2020	Question AnsweringVisual Question Answering	—Unverified
Auto-Parsing Network for Image Captioning and Visual Question Answering	Aug 24, 2021	Image CaptioningQuestion Answering	—Unverified
AVIS: Autonomous Visual Information Seeking with Large Language Model Agent	Jun 13, 2023	Decision MakingLanguage Modeling	—Unverified
A Vision Centric Remote Sensing Benchmark	Mar 20, 2025	Question AnsweringRepresentation Learning	—Unverified

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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