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 1711–1720 of 2167 papers

Title	Date	Tasks	Status	Hype
Multi-Modal Graph Neural Network for Joint Reasoning on Vision and Scene Text	Mar 31, 2020	Graph Neural NetworkQuestion Answering	CodeCode Available	1
Assessing Image Quality Issues for Real-World Problems	Mar 27, 2020	Image CaptioningQuestion Answering	—Unverified	0
P NP, at least in Visual Question Answering	Mar 26, 2020	Question AnsweringVisual Question Answering	CodeCode Available	0
Linguistically Driven Graph Capsule Network for Visual Question Reasoning	Mar 23, 2020	Question AnsweringVisual Question Answering	—Unverified	0
Visual Question Answering for Cultural Heritage	Mar 22, 2020	Question AnsweringVisual Question Answering	—Unverified	0
Normalized and Geometry-Aware Self-Attention Network for Image Captioning	Mar 19, 2020	Image CaptioningMachine Translation	—Unverified	0
RSVQA: Visual Question Answering for Remote Sensing Data	Mar 16, 2020	Land Cover ClassificationObject Counting	—Unverified	0
Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI	Mar 16, 2020	BenchmarkingExplainable Artificial Intelligence (XAI)	CodeCode Available	1
Counterfactual Samples Synthesizing for Robust Visual Question Answering	Mar 14, 2020	counterfactualQuestion Answering	CodeCode Available	1
MQA: Answering the Question via Robotic Manipulation	Mar 10, 2020	Imitation LearningQuestion Answering	CodeCode Available	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