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 1841–1850 of 2167 papers

Title	Date	Tasks	Status
Integrating Knowledge and Reasoning in Image Understanding	Jun 24, 2019	Object RecognitionQuestion Answering	—Unverified
Adversarial Multimodal Network for Movie Question Answering	Jun 24, 2019	Question AnsweringVideo Question Answering	—Unverified
Investigating Biases in Textual Entailment Datasets	Jun 23, 2019	BIG-bench Machine LearningNatural Language Inference	—Unverified
Two-Level Approach for No-Reference Consumer Video Quality Assessment	Jun 20, 2019	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available
Adversarial Regularization for Visual Question Answering: Strengths, Shortcomings, and Side Effects	Jun 20, 2019	Question AnsweringVisual Question Answering	—Unverified
Improving Visual Question Answering by Referring to Generated Paragraph Captions	Jun 14, 2019	DecoderImage Captioning	—Unverified
Mimic and Fool: A Task Agnostic Adversarial Attack	Jun 11, 2019	Adversarial AttackImage Captioning	CodeCode Available
Psycholinguistics meets Continual Learning: Measuring Catastrophic Forgetting in Visual Question Answering	Jun 10, 2019	Continual LearningQuestion Answering	—Unverified
ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering	Jun 6, 2019	Question AnsweringVideo Question Answering	CodeCode Available
Generating Question Relevant Captions to Aid Visual Question Answering	Jun 3, 2019	General KnowledgeImage Captioning	—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