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 1271–1280 of 2167 papers

Title	Date	Tasks	Status	Hype
Webly Supervised Concept Expansion for General Purpose Vision Models	Feb 4, 2022	Human-Object Interaction DetectionImage Retrieval	—Unverified	0
Grounding Answers for Visual Questions Asked by Visually Impaired People	Feb 4, 2022	Question AnsweringVisual Question Answering	CodeCode Available	0
Compositionality as Lexical Symmetry	Jan 30, 2022	Data AugmentationInductive Bias	CodeCode Available	0
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation	Jan 28, 2022	Image CaptioningImage-text matching	CodeCode Available	5
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages	Jan 27, 2022	Cross-Modal RetrievalFew-Shot Learning	CodeCode Available	1
Transformer Module Networks for Systematic Generalization in Visual Question Answering	Jan 27, 2022	Question AnsweringSystematic Generalization	CodeCode Available	0
Learning to Compose Diversified Prompts for Image Emotion Classification	Jan 26, 2022	ClassificationEmotion Classification	—Unverified	0
MGA-VQA: Multi-Granularity Alignment for Visual Question Answering	Jan 25, 2022	Question AnsweringVisual Question Answering	—Unverified	0
SA-VQA: Structured Alignment of Visual and Semantic Representations for Visual Question Answering	Jan 25, 2022	Question AnsweringVisual Question Answering	—Unverified	0
Question Generation for Evaluating Cross-Dataset Shifts in Multi-modal Grounding	Jan 24, 2022	Question AnsweringQuestion Generation	—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