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 731–740 of 2167 papers

Title	Date	Tasks	Status	Hype
Filling the Image Information Gap for VQA: Prompting Large Language Models to Proactively Ask Questions	Nov 20, 2023	Question AnsweringVisual Question Answering	CodeCode Available	0
HIDRO-VQA: High Dynamic Range Oracle for Video Quality Assessment	Nov 18, 2023	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	1
Understanding and Mitigating Classification Errors Through Interpretable Token Patterns	Nov 18, 2023	ClassificationNER	—Unverified	0
Video-LLaVA: Learning United Visual Representation by Alignment Before Projection	Nov 16, 2023	Language ModelingLanguage Modelling	CodeCode Available	4
Attribute Diversity Determines the Systematicity Gap in VQA	Nov 15, 2023	AttributeDiagnostic	CodeCode Available	0
Improving Zero-shot Visual Question Answering via Large Language Models with Reasoning Question Prompts	Nov 15, 2023	Question AnsweringSentence	CodeCode Available	0
Multiple-Question Multiple-Answer Text-VQA	Nov 15, 2023	DecoderDenoising	—Unverified	0
Asking More Informative Questions for Grounded Retrieval	Nov 14, 2023	Question AnsweringQuestion Selection	—Unverified	0
A Comprehensive Evaluation of GPT-4V on Knowledge-Intensive Visual Question Answering	Nov 13, 2023	Decision MakingExplanation Generation	CodeCode Available	1
CLiF-VQA: Enhancing Video Quality Assessment by Incorporating High-Level Semantic Information related to Human Feelings	Nov 13, 2023	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified	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