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 1921–1930 of 2167 papers

Title	Date	Tasks	Status
Do Explanations make VQA Models more Predictable to a Human?	Oct 29, 2018	Question AnsweringVisual Question Answering	—Unverified
TallyQA: Answering Complex Counting Questions	Oct 29, 2018	AttributeObject Counting	CodeCode Available
Knowing Where to Look? Analysis on Attention of Visual Question Answering System	Oct 9, 2018	Question AnsweringVisual Question Answering	—Unverified
Overcoming Language Priors in Visual Question Answering with Adversarial Regularization	Oct 8, 2018	Question AnsweringVisual Grounding	—Unverified
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding	Oct 4, 2018	Question AnsweringRepresentation Learning	CodeCode Available
Transfer Learning via Unsupervised Task Discovery for Visual Question Answering	Oct 3, 2018	Question AnsweringTransfer Learning	CodeCode Available
Multimodal Differential Network for Visual Question Generation	Oct 1, 2018	Image CaptioningNatural Questions	—Unverified
Convolutional Neural Networks for Video Quality Assessment	Sep 26, 2018	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified
Textually Enriched Neural Module Networks for Visual Question Answering	Sep 23, 2018	Image CaptioningQuestion Answering	—Unverified
The Wisdom of MaSSeS: Majority, Subjectivity, and Semantic Similarity in the Evaluation of VQA	Sep 12, 2018	Question AnsweringSemantic Similarity	—Unverified

Show:10 25 50

← PrevPage 193 of 217Next →

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