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 2126–2150 of 2167 papers

Title	Date	Tasks	Status
Annotation Methodologies for Vision and Language Dataset Creation	Jul 10, 2016	Action RecognitionImage Description	—Unverified
Revisiting Visual Question Answering Baselines	Jun 27, 2016	Binary ClassificationMultiple-choice	CodeCode Available
Analyzing the Behavior of Visual Question Answering Models	Jun 23, 2016	Question AnsweringVisual Question Answering	CodeCode Available
Question Relevance in VQA: Identifying Non-Visual And False-Premise Questions	Jun 21, 2016	Question AnsweringQuestion Similarity	—Unverified
DualNet: Domain-Invariant Network for Visual Question Answering	Jun 20, 2016	Question AnsweringVisual Question Answering	—Unverified
FVQA: Fact-based Visual Question Answering	Jun 17, 2016	Common Sense ReasoningQuestion Answering	—Unverified
Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?	Jun 17, 2016	Question AnsweringVisual Question Answering	—Unverified
Training Recurrent Answering Units with Joint Loss Minimization for VQA	Jun 12, 2016	Question AnsweringVisual Question Answering	—Unverified
Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?	Jun 11, 2016	Question AnsweringVisual Question Answering	—Unverified
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding	Jun 6, 2016	Phrase GroundingVisual Grounding	CodeCode Available
Multimodal Residual Learning for Visual QA	Jun 5, 2016	Multiple-choiceQuestion Answering	CodeCode Available
``A Distorted Skull Lies in the Bottom Center...'' Identifying Paintings from Text Descriptions	Jun 1, 2016	Question AnsweringVisual Question Answering (VQA)	—Unverified
Answer-Type Prediction for Visual Question Answering	Jun 1, 2016	Object RecognitionPrediction	—Unverified
End-to-End Instance Segmentation with Recurrent Attention	May 30, 2016	Autonomous DrivingImage Captioning	CodeCode Available
Ask Your Neurons: A Deep Learning Approach to Visual Question Answering	May 9, 2016	Question AnsweringVisual Question Answering	CodeCode Available
Leveraging Visual Question Answering for Image-Caption Ranking	May 4, 2016	Image RetrievalQuestion Answering	—Unverified
Learning Models for Actions and Person-Object Interactions with Transfer to Question Answering	Apr 16, 2016	General ClassificationHuman-Object Interaction Detection	—Unverified
Counting Everyday Objects in Everyday Scenes	Apr 12, 2016	ObjectObject Counting	CodeCode Available
A Focused Dynamic Attention Model for Visual Question Answering	Apr 6, 2016	Question AnsweringVisual Question Answering	—Unverified
Neural Attention Models for Sequence Classification: Analysis and Application to Key Term Extraction and Dialogue Act Detection	Mar 31, 2016	Caption GenerationClassification	—Unverified
A Diagram Is Worth A Dozen Images	Mar 24, 2016	Visual Question Answering (VQA)	CodeCode Available
Image Captioning and Visual Question Answering Based on Attributes and External Knowledge	Mar 9, 2016	General KnowledgeImage Captioning	—Unverified
Dynamic Memory Networks for Visual and Textual Question Answering	Mar 4, 2016	Question AnsweringVisual Question Answering	CodeCode Available
Neural Self Talk: Image Understanding via Continuous Questioning and Answering	Dec 10, 2015	Question AnsweringQuestion Generation	—Unverified
Simple Baseline for Visual Question Answering	Dec 7, 2015	Visual Question AnsweringVisual Question Answering (VQA)	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