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 791–800 of 2167 papers

Title	Date	Tasks	Status
Joint learning of object graph and relation graph for visual question answering	May 9, 2022	AttributeGraph Neural Network	—Unverified
Generating and Evaluating Explanations of Attended and Error-Inducing Input Regions for VQA Models	Mar 26, 2021	Question AnsweringVisual Question Answering	—Unverified
Compressing Visual-linguistic Model via Knowledge Distillation	Apr 5, 2021	Image CaptioningKnowledge Distillation	—Unverified
Grounded Word Sense Translation	Jun 1, 2019	Grounded language learningMachine Translation	—Unverified
It Takes Two to Tango: Towards Theory of AI's Mind	Apr 3, 2017	AttributeQuestion Answering	—Unverified
Griffon-G: Bridging Vision-Language and Vision-Centric Tasks via Large Multimodal Models	Oct 21, 2024	Instruction Followingobject-detection	—Unverified
How to Design Sample and Computationally Efficient VQA Models	Mar 22, 2021	Question AnsweringVisual Question Answering	—Unverified
Co-VQA : Answering by Interactive Sub Question Sequence	Nov 16, 2021	Question AnsweringVisual Question Answering	—Unverified
A Thousand Words Are Worth More Than a Picture: Natural Language-Centric Outside-Knowledge Visual Question Answering	Jan 14, 2022	Generative Question AnsweringImage to text	—Unverified
iVQA: Inverse Visual Question Answering	Oct 10, 2017	Question AnsweringQuestion Generation	—Unverified

Show:10 25 50

← PrevPage 80 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