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 2076–2100 of 2167 papers

Title	Date	Tasks	Status
Inferring and Executing Programs for Visual Reasoning	May 10, 2017	Visual Question Answering (VQA)Visual Reasoning	CodeCode Available
Survey of Visual Question Answering: Datasets and Techniques	May 10, 2017	Deep LearningQuestion Answering	—Unverified
The Forgettable-Watcher Model for Video Question Answering	May 3, 2017	modelQuestion Answering	—Unverified
Speech-Based Visual Question Answering	May 1, 2017	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available
The Promise of Premise: Harnessing Question Premises in Visual Question Answering	May 1, 2017	Question AnsweringRelevance Detection	CodeCode Available
C-VQA: A Compositional Split of the Visual Question Answering (VQA) v1.0 Dataset	Apr 26, 2017	Question AnsweringVisual Question Answering	—Unverified
Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets	Apr 24, 2017	Multiple-choiceQuestion Answering	—Unverified
Learning to Reason: End-to-End Module Networks for Visual Question Answering	Apr 18, 2017	Visual DialogVisual Question Answering	CodeCode Available
TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering	Apr 14, 2017	Question AnsweringVisual Question Answering	CodeCode Available
ShapeWorld - A new test methodology for multimodal language understanding	Apr 14, 2017	Multimodal Deep LearningVisual Question Answering	CodeCode Available
What's in a Question: Using Visual Questions as a Form of Supervision	Apr 12, 2017	Data AugmentationForm	CodeCode Available
Show, Ask, Attend, and Answer: A Strong Baseline For Visual Question Answering	Apr 11, 2017	Visual Question AnsweringVisual Question Answering (VQA)	CodeCode Available
An Empirical Evaluation of Visual Question Answering for Novel Objects	Apr 8, 2017	Question AnsweringVisual Question Answering	—Unverified
It Takes Two to Tango: Towards Theory of AI's Mind	Apr 3, 2017	AttributeQuestion Answering	—Unverified
Aligned Image-Word Representations Improve Inductive Transfer Across Vision-Language Tasks	Apr 2, 2017	Multi-Task LearningQuestion Answering	—Unverified
An Analysis of Visual Question Answering Algorithms	Mar 28, 2017	Question AnsweringVisual Question Answering	—Unverified
Multimodal Compact Bilinear Pooling for Multimodal Neural Machine Translation	Mar 23, 2017	DecoderMachine Translation	—Unverified
Recurrent and Contextual Models for Visual Question Answering	Mar 23, 2017	DiversityMultiple-choice	—Unverified
VQABQ: Visual Question Answering by Basic Questions	Mar 19, 2017	Question AnsweringVisual Question Answering	—Unverified
End-to-end optimization of goal-driven and visually grounded dialogue systems	Mar 15, 2017	DecoderDeep Reinforcement Learning	CodeCode Available
Tree Memory Networks for Modelling Long-term Temporal Dependencies	Mar 12, 2017	Machine TranslationPart-Of-Speech Tagging	—Unverified
Task-driven Visual Saliency and Attention-based Visual Question Answering	Feb 22, 2017	Question AnsweringVisual Question Answering	—Unverified
Vision and Language Integration: Moving beyond Objects	Jan 1, 2017	Action ClassificationImage Captioning	—Unverified
The VQA-Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions	Dec 16, 2016	BIG-bench Machine LearningQuestion Answering	—Unverified
Attentive Explanations: Justifying Decisions and Pointing to the Evidence	Dec 14, 2016	Decision MakingQuestion 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