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 1761–1770 of 2167 papers

Title	Date	Tasks	Status
XGPT: Cross-modal Generative Pre-Training for Image Captioning	Mar 3, 2020	Data AugmentationDenoising	—Unverified
A Question-Centric Model for Visual Question Answering in Medical Imaging	Mar 2, 2020	Medical Image AnalysisQuestion Answering	CodeCode Available
A Study on Multimodal and Interactive Explanations for Visual Question Answering	Mar 1, 2020	Explainable Artificial Intelligence (XAI)Prediction	—Unverified
Unshuffling Data for Improved Generalization	Feb 27, 2020	ClusteringData Augmentation	—Unverified
What BERT Sees: Cross-Modal Transfer for Visual Question Generation	Feb 25, 2020	Question GenerationQuestion-Generation	—Unverified
A Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality Assessment	Feb 25, 2020	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified
On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering	Feb 24, 2020	Question AnsweringReferring Expression	—Unverified
VQA-LOL: Visual Question Answering under the Lens of Logic	Feb 19, 2020	NegationQuestion Answering	—Unverified
CQ-VQA: Visual Question Answering on Categorized Questions	Feb 17, 2020	Question AnsweringVisual Question Answering	—Unverified
Sparse and Structured Visual Attention	Feb 13, 2020	Image CaptioningQuestion 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