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 1951–1975 of 2167 papers

Title	Date	Tasks	Status
Deep Attention Neural Tensor Network for Visual Question Answering	Sep 1, 2018	Deep AttentionQuestion Answering	—Unverified
Deep Video Quality Assessor: From Spatio-temporal Visual Sensitivity to A Convolutional Neural Aggregation Network	Sep 1, 2018	SensitivityVideo Quality Assessment	—Unverified
Adapting Visual Question Answering Models for Enhancing Multimodal Community Q&A Platforms	Aug 29, 2018	Community Question AnsweringGeneral Classification	CodeCode Available
Question-Guided Hybrid Convolution for Visual Question Answering	Aug 8, 2018	Question AnsweringVisual Question Answering	—Unverified
A Joint Sequence Fusion Model for Video Question Answering and Retrieval	Aug 7, 2018	DecoderMultiple-choice	CodeCode Available
Visual Reasoning with Multi-hop Feature Modulation	Aug 3, 2018	Question AnsweringVisual Dialog	CodeCode Available
A Reinforcement Learning Framework for Natural Question Generation using Bi-discriminators	Aug 1, 2018	AttributeNatural Questions	—Unverified
Visual Question Answering Dataset for Bilingual Image Understanding: A Study of Cross-Lingual Transfer Using Attention Maps	Aug 1, 2018	Cross-Lingual TransferImage Captioning	—Unverified
Learning Visual Question Answering by Bootstrapping Hard Attention	Aug 1, 2018	Hard AttentionQuestion Answering	CodeCode Available
Interpretable Visual Question Answering by Visual Grounding from Attention Supervision Mining	Aug 1, 2018	Question AnsweringVisual Grounding	—Unverified
Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning Model	Jul 29, 2018	Visual Question Answering (VQA)	CodeCode Available
Question Relevance in Visual Question Answering	Jul 23, 2018	Question AnsweringVisual Question Answering	—Unverified
Latent Alignment and Variational Attention	Jul 10, 2018	Hard AttentionMachine Translation	CodeCode Available
NMT-Keras: a Very Flexible Toolkit with a Focus on Interactive NMT and Online Learning	Jul 9, 2018	General ClassificationMachine Translation	—Unverified
Connecting Language and Vision to Actions	Jul 1, 2018	Image CaptioningLanguage Modeling	—Unverified
Semantically Equivalent Adversarial Rules for Debugging NLP models	Jul 1, 2018	Data AugmentationQuestion Answering	CodeCode Available
Bridging Languages through Images with Deep Partial Canonical Correlation Analysis	Jul 1, 2018	Image DescriptionImage Retrieval	CodeCode Available
Multimodal Neural Machine Translation for Low-resource Language Pairs using Synthetic Data	Jul 1, 2018	Image DescriptionMachine Translation	—Unverified
Pushing the Limits of Radiology with Joint Modeling of Visual and Textual Information	Jul 1, 2018	Image ClassificationMachine Translation	—Unverified
End-to-End Audio Visual Scene-Aware Dialog using Multimodal Attention-Based Video Features	Jun 21, 2018	Question AnsweringVideo Description	CodeCode Available
Learning Conditioned Graph Structures for Interpretable Visual Question Answering	Jun 19, 2018	Question AnsweringVisual Question Answering	CodeCode Available
Learning Visual Knowledge Memory Networks for Visual Question Answering	Jun 13, 2018	Question AnsweringVisual Question Answering	—Unverified
iParaphrasing: Extracting Visually Grounded Paraphrases via an Image	Jun 12, 2018	Image CaptioningQuestion Answering	CodeCode Available
Learning Answer Embeddings for Visual Question Answering	Jun 10, 2018	Question AnsweringTransfer Learning	—Unverified
Cross-Dataset Adaptation for Visual Question Answering	Jun 10, 2018	Domain AdaptationQuestion Answering	—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