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 1291–1300 of 2167 papers

Title	Date	Tasks	Status	Hype
Uni-EDEN: Universal Encoder-Decoder Network by Multi-Granular Vision-Language Pre-training	Jan 11, 2022	DecoderImage Captioning	—Unverified	0
COIN: Counterfactual Image Generation for VQA Interpretation	Jan 10, 2022	counterfactualImage Generation	—Unverified	0
FAVER: Blind Quality Prediction of Variable Frame Rate Videos	Jan 5, 2022	Cloud ComputingVideo Quality Assessment	CodeCode Available	1
Interactive Attention AI to translate low light photos to captions for night scene understanding in women safety	Jan 4, 2022	DecoderDeep Learning	—Unverified	0
V-Doc: Visual Questions Answers With Documents	Jan 1, 2022	Question AnsweringQuestion Generation	—Unverified	0
Transform-Retrieve-Generate: Natural Language-Centric Outside-Knowledge Visual Question Answering	Jan 1, 2022	Generative Question AnsweringImage to text	—Unverified	0
Query and Attention Augmentation for Knowledge-Based Explainable Reasoning	Jan 1, 2022	Question AnsweringVisual Question Answering	CodeCode Available	0
Towards General Purpose Vision Systems: An End-to-End Task-Agnostic Vision-Language Architecture	Jan 1, 2022	Question AnsweringVisual Question Answering	—Unverified	0
Maintaining Reasoning Consistency in Compositional Visual Question Answering	Jan 1, 2022	Question AnsweringVisual Question Answering	CodeCode Available	1
Multi-Image Visual Question Answering	Dec 27, 2021	Question AnsweringVisual Question 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