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 2001–2025 of 2167 papers

Title	Date	Tasks	Status
Two can play this Game: Visual Dialog with Discriminative Question Generation and Answering	Mar 29, 2018	Image CaptioningQuestion Answering	—Unverified
Motion-Appearance Co-Memory Networks for Video Question Answering	Mar 29, 2018	Question AnsweringVideo Question Answering	—Unverified
Generalized Hadamard-Product Fusion Operators for Visual Question Answering	Mar 26, 2018	Neural Architecture SearchQuestion Answering	—Unverified
Explicit Reasoning over End-to-End Neural Architectures for Visual Question Answering	Mar 23, 2018	Question AnsweringVisual Question Answering	—Unverified
Attention on Attention: Architectures for Visual Question Answering (VQA)	Mar 21, 2018	GPUQuestion Answering	CodeCode Available
VQA-E: Explaining, Elaborating, and Enhancing Your Answers for Visual Questions	Mar 20, 2018	Explanatory Visual Question AnsweringMulti-Task Learning	—Unverified
A Dataset and Architecture for Visual Reasoning with a Working Memory	Mar 16, 2018	DiagnosticLogical Reasoning	CodeCode Available
Inverse Visual Question Answering: A New Benchmark and VQA Diagnosis Tool	Mar 16, 2018	Question AnsweringReinforcement Learning	—Unverified
Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning	Mar 14, 2018	Question AnsweringVisual Question Answering	CodeCode Available
VizWiz Grand Challenge: Answering Visual Questions from Blind People	Feb 22, 2018	Question AnsweringVisual Question Answering	CodeCode Available
Multimodal Explanations: Justifying Decisions and Pointing to the Evidence	Feb 15, 2018	Activity RecognitionExplainable Models	CodeCode Available
Learning to Count Objects in Natural Images for Visual Question Answering	Feb 15, 2018	Visual Question AnsweringVisual Question Answering (VQA)	CodeCode Available
Generating Triples with Adversarial Networks for Scene Graph Construction	Feb 7, 2018	Attributegraph construction	—Unverified
Dual Recurrent Attention Units for Visual Question Answering	Feb 1, 2018	Question AnsweringVisual Question Answering	CodeCode Available
Game of Sketches: Deep Recurrent Models of Pictionary-style Word Guessing	Jan 29, 2018	Question AnsweringVisual Question Answering	CodeCode Available
Object-based reasoning in VQA	Jan 29, 2018	Objectobject-detection	—Unverified
Tell-and-Answer: Towards Explainable Visual Question Answering using Attributes and Captions	Jan 27, 2018	AttributeImage Captioning	—Unverified
Structured Triplet Learning with POS-tag Guided Attention for Visual Question Answering	Jan 24, 2018	Multiple-choicePOS	CodeCode Available
DVQA: Understanding Data Visualizations via Question Answering	Jan 24, 2018	ArticlesChart Question Answering	CodeCode Available
Not-So-CLEVR: Visual Relations Strain Feedforward Neural Networks	Jan 1, 2018	MemorizationQuestion Answering	—Unverified
Interpretable Counting for Visual Question Answering	Dec 23, 2017	Question AnsweringVisual Question Answering	—Unverified
Visual Explanations from Hadamard Product in Multimodal Deep Networks	Dec 18, 2017	Question AnsweringVisual Question Answering	—Unverified
IQA: Visual Question Answering in Interactive Environments	Dec 9, 2017	NavigateReinforcement Learning	CodeCode Available
Learning by Asking Questions	Dec 4, 2017	Question AnsweringVisual Question Answering	—Unverified
Incorporating External Knowledge to Answer Open-Domain Visual Questions with Dynamic Memory Networks	Dec 3, 2017	Question AnsweringVisual Question 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