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 1681–1690 of 2167 papers

Title	Date	Tasks	Status	Hype
Structured Multimodal Attentions for TextVQA	Jun 1, 2020	Graph AttentionOptical Character Recognition (OCR)	CodeCode Available	1
Multimodal grid features and cell pointers for Scene Text Visual Question Answering	Jun 1, 2020	Question AnsweringVisual Question Answering	—Unverified	0
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content	May 29, 2020	Benchmarkingfeature selection	CodeCode Available	1
On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law	May 19, 2020	Model SelectionQuestion Answering	—Unverified	0
Visual Relationship Detection using Scene Graphs: A Survey	May 16, 2020	Graph GenerationImage Generation	—Unverified	0
Cross-Modality Relevance for Reasoning on Language and Vision	May 12, 2020	Question AnsweringVisual Question Answering	CodeCode Available	1
COBRA: Contrastive Bi-Modal Representation Algorithm	May 7, 2020	Cross-Modal RetrievalImage Captioning	CodeCode Available	1
Visual Question Answering with Prior Class Semantics	May 4, 2020	Question AnsweringVisual Question Answering	—Unverified	0
A Corpus for Visual Question Answering Annotated with Frame Semantic Information	May 1, 2020	Question AnsweringVisual Question Answering	—Unverified	0
Image Position Prediction in Multimodal Documents	May 1, 2020	ArticlesCaption Generation	—Unverified	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