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 1891–1900 of 2167 papers

Title	Date	Tasks	Status
Generating Natural Language Explanations for Visual Question Answering using Scene Graphs and Visual Attention	Feb 15, 2019	Explanation GenerationLanguage Modeling	—Unverified
Cycle-Consistency for Robust Visual Question Answering	Feb 15, 2019	Question AnsweringQuestion Generation	—Unverified
Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded	Feb 11, 2019	Image CaptioningQuestion Answering	—Unverified
VrR-VG: Refocusing Visually-Relevant Relationships	Feb 1, 2019	Image CaptioningQuestion Answering	—Unverified
BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection	Jan 31, 2019	Question AnsweringRelationship Detection	CodeCode Available
Visual Entailment: A Novel Task for Fine-Grained Image Understanding	Jan 20, 2019	Natural Language InferenceQuestion Answering	—Unverified
CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions	Jan 3, 2019	DiagnosticImage Segmentation	CodeCode Available
The meaning of "most" for visual question answering models	Dec 31, 2018	Question AnsweringVisual Question Answering	—Unverified
Scene Graph Reasoning with Prior Visual Relationship for Visual Question Answering	Dec 23, 2018	Cross-Modal Information RetrievalInformation Retrieval	—Unverified
Focal Visual-Text Attention for Memex Question Answering	Dec 14, 2018	Memex Question AnsweringQuestion Answering	CodeCode Available

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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