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 1211–1220 of 2167 papers

Title	Date	Tasks	Status
Locate Then Generate: Bridging Vision and Language with Bounding Box for Scene-Text VQA	Apr 4, 2023	Answer GenerationLanguage Modelling	—Unverified
Logically Consistent Loss for Visual Question Answering	Nov 19, 2020	Multi-Task LearningQuestion Answering	—Unverified
LoGra-Med: Long Context Multi-Graph Alignment for Medical Vision-Language Model	Oct 3, 2024	image-classificationImage Classification	—Unverified
LOIS: Looking Out of Instance Semantics for Visual Question Answering	Jul 26, 2023	Question AnsweringVisual Question Answering	—Unverified
Long-Form Answers to Visual Questions from Blind and Low Vision People	Aug 12, 2024	FormVisual Question Answering (VQA)	—Unverified
Look, Learn and Leverage (L^3): Mitigating Visual-Domain Shift and Discovering Intrinsic Relations via Symbolic Alignment	Aug 30, 2024	Question AnsweringRepresentation Learning	—Unverified
Look, Read and Ask: Learning to Ask Questions by Reading Text in Images	Nov 23, 2022	Optical Character Recognition (OCR)Question Answering	—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
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey	Dec 18, 2021	Data AugmentationFew-Shot Learning	—Unverified
LRRA:A Transparent Neural-Symbolic Reasoning Framework for Real-World Visual Question Answering	Aug 1, 2021	Question AnsweringVisual Question Answering	—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