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

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Showing 1221–1230 of 2167 papers

Title	Date	Tasks	Status
A reinforcement learning approach for VQA validation: an application to diabetic macular edema grading	Jul 19, 2023	Medical Image AnalysisQuestion Answering	—Unverified
A Reinforcement Learning Framework for Natural Question Generation using Bi-discriminators	Aug 1, 2018	AttributeNatural Questions	—Unverified
A Restricted Visual Turing Test for Deep Scene and Event Understanding	Dec 6, 2015	Question AnsweringVideo Captioning	—Unverified
A review of Quantum Neural Networks: Methods, Models, Dilemma	Sep 4, 2021	Computational EfficiencyVisual Question Answering (VQA)	—Unverified
Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions	Jun 8, 2021	Question AnsweringVisual Question Answering	—Unverified
Are we asking the right questions in MovieQA?	Nov 8, 2019	Question AnsweringVisual Question Answering	—Unverified
Are we pretraining it right? Digging deeper into visio-linguistic pretraining	Apr 19, 2020	Visual Question Answering (VQA)	—Unverified
Are You Smarter Than a Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension	Jul 1, 2017	Question AnsweringReading Comprehension	—Unverified
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question	Dec 1, 2015	Question AnsweringSentence	—Unverified
Are You Talking to Me? Reasoned Visual Dialog Generation through Adversarial Learning	Nov 21, 2017	Question AnsweringReinforcement Learning	—Unverified

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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