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 1201–1225 of 2167 papers

Title	Date	Tasks	Status	Hype
Making Video Quality Assessment Models Sensitive to Frame Rate Distortions	May 21, 2022	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified	0
Gender and Racial Bias in Visual Question Answering Datasets	May 17, 2022	Question AnsweringVisual Question Answering	—Unverified	0
A Neuro-Symbolic ASP Pipeline for Visual Question Answering	May 16, 2022	Question AnsweringVisual Question Answering	CodeCode Available	0
A Framework to Map VMAF with the Probability of Just Noticeable Difference between Video Encoding Recipes	May 16, 2022	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified	0
Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures	May 10, 2022	AutoMLBIG-bench Machine Learning	—Unverified	0
Learning to Answer Visual Questions from Web Videos	May 10, 2022	Dataset GenerationQuestion Answering	CodeCode Available	1
Joint learning of object graph and relation graph for visual question answering	May 9, 2022	AttributeGraph Neural Network	—Unverified	0
Deep Quality Assessment of Compressed Videos: A Subjective and Objective Study	May 7, 2022	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified	0
QLEVR: A Diagnostic Dataset for Quantificational Language and Elementary Visual Reasoning	May 6, 2022	DiagnosticQuestion Answering	CodeCode Available	0
From Easy to Hard: Learning Language-guided Curriculum for Visual Question Answering on Remote Sensing Data	May 6, 2022	Question AnsweringVisual Question Answering	—Unverified	0
What is Right for Me is Not Yet Right for You: A Dataset for Grounding Relative Directions via Multi-Task Learning	May 5, 2022	Multi-Task LearningQuestion Answering	CodeCode Available	0
LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural Networks	May 5, 2022	Combinatorial OptimizationVisual Question Answering (VQA)	CodeCode Available	0
Declaration-based Prompt Tuning for Visual Question Answering	May 5, 2022	Image-text matchingLanguage Modeling	CodeCode Available	1
All You May Need for VQA are Image Captions	May 4, 2022	AllImage Captioning	CodeCode Available	3
CoCa: Contrastive Captioners are Image-Text Foundation Models	May 4, 2022	Action ClassificationDecoder	CodeCode Available	1
Answer-Me: Multi-Task Open-Vocabulary Visual Question Answering	May 2, 2022	DecoderImage Captioning	—Unverified	0
Vision-Language Pretraining: Current Trends and the Future	May 1, 2022	Question AnsweringRepresentation Learning	—Unverified	0
ViLMedic: a framework for research at the intersection of vision and language in medical AI	May 1, 2022	Medical Visual Question AnsweringQuestion Answering	—Unverified	0
DuReader_vis: A Chinese Dataset for Open-domain Document Visual Question Answering	May 1, 2022	document understandingOpen-Domain Question Answering	—Unverified	0
Bridging the Gap between Recognition-level Pre-training and Commonsensical Vision-language Tasks	May 1, 2022	DiversityInformativeness	—Unverified	0
Flamingo: a Visual Language Model for Few-Shot Learning	Apr 29, 2022	Few-Shot LearningGenerative Visual Question Answering	CodeCode Available	4
Reliable Visual Question Answering: Abstain Rather Than Answer Incorrectly	Apr 28, 2022	Question AnsweringVisual Question Answering	CodeCode Available	1
GRIT: General Robust Image Task Benchmark	Apr 28, 2022	Instance SegmentationKeypoint Detection	CodeCode Available	1
RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning	Apr 24, 2022	Human-Object Interaction DetectionObject	CodeCode Available	1
Hypergraph Transformer: Weakly-supervised Multi-hop Reasoning for Knowledge-based Visual Question Answering	Apr 22, 2022	Question AnsweringVisual Question Answering	CodeCode Available	1

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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