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 1726–1750 of 2167 papers

Title	Date	Tasks	Status
Improving VQA and its Explanations \\ by Comparing Competing Explanations	Jun 28, 2020	Question AnsweringVisual Question Answering	—Unverified
Self-Segregating and Coordinated-Segregating Transformer for Focused Deep Multi-Modular Network for Visual Question Answering	Jun 25, 2020	DiversityQuestion Answering	—Unverified
Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning"	Jun 20, 2020	Graph GenerationQuestion Answering	—Unverified
Mucko: Multi-Layer Cross-Modal Knowledge Reasoning for Fact-based Visual Question Answering	Jun 16, 2020	Question AnsweringVisual Question Answering	—Unverified
ORD: Object Relationship Discovery for Visual Dialogue Generation	Jun 15, 2020	Dialogue GenerationGraph Attention	—Unverified
Exploring Weaknesses of VQA Models through Attribution Driven Insights	Jun 11, 2020	Question AnsweringVisual Question Answering	—Unverified
Estimating semantic structure for the VQA answer space	Jun 10, 2020	General ClassificationQuestion Answering	—Unverified
TA-Student VQA: Multi-Agents Training by Self-Questioning	Jun 1, 2020	DiversityQuestion Answering	—Unverified
Multimodal grid features and cell pointers for Scene Text Visual Question Answering	Jun 1, 2020	Question AnsweringVisual Question Answering	—Unverified
Counterfactual Vision and Language Learning	Jun 1, 2020	counterfactualQuestion Answering	—Unverified
On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law	May 19, 2020	Model SelectionQuestion Answering	—Unverified
Visual Relationship Detection using Scene Graphs: A Survey	May 16, 2020	Graph GenerationImage Generation	—Unverified
Visual Question Answering with Prior Class Semantics	May 4, 2020	Question AnsweringVisual Question Answering	—Unverified
Visuo-Linguistic Question Answering (VLQA) Challenge	May 1, 2020	Question AnsweringReading Comprehension	CodeCode Available
Image Position Prediction in Multimodal Documents	May 1, 2020	ArticlesCaption Generation	—Unverified
A Corpus for Visual Question Answering Annotated with Frame Semantic Information	May 1, 2020	Question AnsweringVisual Question Answering	—Unverified
Pragmatic Issue-Sensitive Image Captioning	Apr 29, 2020	DescriptiveImage Captioning	CodeCode Available
A Novel Attention-based Aggregation Function to Combine Vision and Language	Apr 27, 2020	General ClassificationImage Captioning	—Unverified
MoVie: Revisiting Modulated Convolutions for Visual Counting and Beyond	Apr 24, 2020	Object CountingQuestion Answering	CodeCode Available
Visual Question Answering Using Semantic Information from Image Descriptions	Apr 23, 2020	Question AnsweringVisual Question Answering	—Unverified
Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision	Apr 20, 2020	counterfactualimage-classification	—Unverified
Are we pretraining it right? Digging deeper into visio-linguistic pretraining	Apr 19, 2020	Visual Question Answering (VQA)	—Unverified
Knowledge-Based Visual Question Answering in Videos	Apr 17, 2020	Question AnsweringVideo Question Answering	—Unverified
An Entropy Clustering Approach for Assessing Visual Question Difficulty	Apr 12, 2020	ClusteringQuestion Answering	CodeCode Available
Rephrasing visual questions by specifying the entropy of the answer distribution	Apr 10, 2020	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