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 1651–1675 of 2167 papers

Title	Date	Tasks	Status	Hype
Applying recent advances in Visual Question Answering to Record Linkage	Jul 12, 2020	Question AnsweringVisual Question Answering	CodeCode Available	0
Image Captioning with Compositional Neural Module Networks	Jul 10, 2020	Image CaptioningQuestion Answering	—Unverified	0
IQ-VQA: Intelligent Visual Question Answering	Jul 8, 2020	Question AnsweringVisual Question Answering	CodeCode Available	0
Eliminating Catastrophic Interference with Biased Competition	Jul 3, 2020	Question AnsweringVisual Question Answering	—Unverified	0
Visual Question Answering as a Multi-Task Problem	Jul 3, 2020	Question AnsweringVisual Question Answering	—Unverified	0
Scene Graph Reasoning for Visual Question Answering	Jul 2, 2020	NavigateQuestion Answering	—Unverified	0
The Impact of Explanations on AI Competency Prediction in VQA	Jul 2, 2020	AI AgentLanguage Modeling	—Unverified	0
DocVQA: A Dataset for VQA on Document Images	Jul 1, 2020	Question AnsweringReading Comprehension	CodeCode Available	1
Towards Visual Dialog for Radiology	Jul 1, 2020	Question AnsweringVisual Dialog	—Unverified	0
Visual Question Generation from Radiology Images	Jul 1, 2020	Image AugmentationQuestion Generation	CodeCode Available	1
Aligned Dual Channel Graph Convolutional Network for Visual Question Answering	Jul 1, 2020	Question AnsweringVisual Question Answering	—Unverified	0
Multimodal Neural Graph Memory Networks for Visual Question Answering	Jul 1, 2020	Graph Neural NetworkQuestion Answering	—Unverified	0
ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph	Jun 30, 2020	AttributePrediction	—Unverified	0
Ontology-guided Semantic Composition for Zero-Shot Learning	Jun 30, 2020	image-classificationImage Classification	CodeCode Available	1
Improving VQA and its Explanations \\ by Comparing Competing Explanations	Jun 28, 2020	Question AnsweringVisual Question Answering	—Unverified	0
Graph Optimal Transport for Cross-Domain Alignment	Jun 26, 2020	Graph MatchingImage Captioning	CodeCode Available	1
Self-Segregating and Coordinated-Segregating Transformer for Focused Deep Multi-Modular Network for Visual Question Answering	Jun 25, 2020	DiversityQuestion Answering	—Unverified	0
Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning"	Jun 20, 2020	Graph GenerationQuestion Answering	—Unverified	0
Mucko: Multi-Layer Cross-Modal Knowledge Reasoning for Fact-based Visual Question Answering	Jun 16, 2020	Question AnsweringVisual Question Answering	—Unverified	0
ORD: Object Relationship Discovery for Visual Dialogue Generation	Jun 15, 2020	Dialogue GenerationGraph Attention	—Unverified	0
Sparse and Continuous Attention Mechanisms	Jun 12, 2020	Machine TranslationQuestion Answering	CodeCode Available	1
Large-Scale Adversarial Training for Vision-and-Language Representation Learning	Jun 11, 2020	Image-text RetrievalQuestion Answering	CodeCode Available	1
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning	Jun 11, 2020	Question AnsweringReinforcement Learning (RL)	CodeCode Available	1
Exploring Weaknesses of VQA Models through Attribution Driven Insights	Jun 11, 2020	Question AnsweringVisual Question Answering	—Unverified	0
Estimating semantic structure for the VQA answer space	Jun 10, 2020	General ClassificationQuestion Answering	—Unverified	0

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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