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 2026–2050 of 2167 papers

Title	Date	Tasks	Status	Hype
Attentive Explanations: Justifying Decisions and Pointing to the Evidence (Extended Abstract)	Nov 17, 2017	Question AnsweringVisual Question Answering (VQA)	—Unverified	0
A Novel Framework for Robustness Analysis of Visual QA Models	Nov 16, 2017	Question AnsweringVisual Question Answering	—Unverified	0
High-Order Attention Models for Visual Question Answering	Nov 12, 2017	Question AnsweringVisual Question Answering	CodeCode Available	0
Active Learning for Visual Question Answering: An Empirical Study	Nov 6, 2017	Active LearningVisual Question Answering	CodeCode Available	0
FigureQA: An Annotated Figure Dataset for Visual Reasoning	Oct 19, 2017	BIG-bench Machine LearningChart Question Answering	CodeCode Available	0
iVQA: Inverse Visual Question Answering	Oct 10, 2017	Question AnsweringQuestion Generation	—Unverified	0
Fooling Vision and Language Models Despite Localization and Attention Mechanism	Sep 25, 2017	Dense CaptioningNatural Language Understanding	—Unverified	0
Survey of Recent Advances in Visual Question Answering	Sep 24, 2017	Question AnsweringSurvey	—Unverified	0
Visual Reference Resolution using Attention Memory for Visual Dialog	Sep 23, 2017	Parameter PredictionQuestion Answering	—Unverified	0
FiLM: Visual Reasoning with a General Conditioning Layer	Sep 22, 2017	Image Retrieval with Multi-Modal QueryVisual Question Answering (VQA)	CodeCode Available	1
Visual Question Generation as Dual Task of Visual Question Answering	Sep 21, 2017	Question AnsweringQuestion Generation	—Unverified	0
Exploring Human-like Attention Supervision in Visual Question Answering	Sep 19, 2017	Question AnsweringVisual Question Answering	—Unverified	0
Robustness Analysis of Visual QA Models by Basic Questions	Sep 14, 2017	Question AnsweringVisual Question Answering	—Unverified	0
Memory Augmented Neural Networks for Natural Language Processing	Sep 1, 2017	AI AgentLanguage Modeling	—Unverified	0
Spatial Language Understanding with Multimodal Graphs using Declarative Learning based Programming	Sep 1, 2017	Image CaptioningImage Retrieval	—Unverified	0
Data Augmentation for Visual Question Answering	Sep 1, 2017	Data AugmentationGeneral Classification	—Unverified	0
Sheffield MultiMT: Using Object Posterior Predictions for Multimodal Machine Translation	Sep 1, 2017	Image CaptioningImage Classification	—Unverified	0
VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation	Aug 15, 2017	Language ModelingLanguage Modelling	CodeCode Available	0
Beyond Bilinear: Generalized Multimodal Factorized High-order Pooling for Visual Question Answering	Aug 10, 2017	Question AnsweringVisual Question Answering	CodeCode Available	0
Learning to Disambiguate by Asking Discriminative Questions	Aug 9, 2017	BenchmarkingImage Captioning	—Unverified	0
Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge	Aug 9, 2017	GPUVisual Question Answering	CodeCode Available	0
Structured Attentions for Visual Question Answering	Aug 7, 2017	Visual Question AnsweringVisual Question Answering (VQA)	CodeCode Available	0
Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answering	Aug 4, 2017	Question AnsweringVisual Question Answering	CodeCode Available	0
A Simple Loss Function for Improving the Convergence and Accuracy of Visual Question Answering Models	Aug 2, 2017	Question AnsweringVisual Question Answering	CodeCode Available	0
Spectral Graph-Based Method of Multimodal Word Embedding	Aug 1, 2017	Graph EmbeddingImage Retrieval	—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	InternVL-C	Accuracy	81.2	—	Unverified
10	Lyrics	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