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 581–590 of 2167 papers

Title	Date	Tasks	Status	Hype
Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments	Nov 20, 2017	Reinforcement LearningTranslation	CodeCode Available	1
FiLM: Visual Reasoning with a General Conditioning Layer	Sep 22, 2017	Image Retrieval with Multi-Modal QueryVisual Question Answering (VQA)	CodeCode Available	1
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering	Jul 25, 2017	Image CaptioningVisual Question Answering	CodeCode Available	1
ParlAI: A Dialog Research Software Platform	May 18, 2017	reinforcement-learningReinforcement Learning	CodeCode Available	1
Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning	Mar 20, 2017	Deep Reinforcement Learningreinforcement-learning	CodeCode Available	1
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning	Dec 20, 2016	DiagnosticQuestion Answering	CodeCode Available	1
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization	Oct 7, 2016	General ClassificationImage Attribution	CodeCode Available	1
Hierarchical Question-Image Co-Attention for Visual Question Answering	May 31, 2016	Visual DialogVisual Question Answering	CodeCode Available	1
Stacked Attention Networks for Image Question Answering	Nov 7, 2015	Visual Question Answering (VQA)	CodeCode Available	1
VQA: Visual Question Answering	May 3, 2015	Image CaptioningMultiple-choice	CodeCode Available	1

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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