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 971–980 of 2167 papers

Title	Date	Tasks	Status
LRRA:A Transparent Neural-Symbolic Reasoning Framework for Real-World Visual Question Answering	Aug 1, 2021	Question AnsweringVisual Question Answering	—Unverified
Asking questions on handwritten document collections	Oct 2, 2021	Optical Character Recognition (OCR)Question Answering	—Unverified
CoBIT: A Contrastive Bi-directional Image-Text Generation Model	Mar 23, 2023	DecoderImage Generation	—Unverified
Look, Read and Ask: Learning to Ask Questions by Reading Text in Images	Nov 23, 2022	Optical Character Recognition (OCR)Question Answering	—Unverified
FVQA: Fact-based Visual Question Answering	Jun 17, 2016	Common Sense ReasoningQuestion Answering	—Unverified
LEAF-QA: Locate, Encode & Attend for Figure Question Answering	Jul 30, 2019	Chart Question AnsweringQuestion Answering	—Unverified
FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering	Mar 19, 2023	Common Sense ReasoningInformation Retrieval	—Unverified
Losing Visual Needles in Image Haystacks: Vision Language Models are Easily Distracted in Short and Long Contexts	Jun 24, 2024	Mathematical ReasoningVisual Question Answering (VQA)	—Unverified
Fusion of Domain-Adapted Vision and Language Models for Medical Visual Question Answering	Apr 24, 2024	Language ModelingLanguage Modelling	—Unverified
Fusion of Detected Objects in Text for Visual Question Answering	Aug 14, 2019	Question AnsweringVisual Commonsense Reasoning	—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	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