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 2051–2075 of 2167 papers

Title	Date	Tasks	Status
VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation	Aug 15, 2017	Language ModelingLanguage Modelling	CodeCode Available
Beyond Bilinear: Generalized Multimodal Factorized High-order Pooling for Visual Question Answering	Aug 10, 2017	Question AnsweringVisual Question Answering	CodeCode Available
Learning to Disambiguate by Asking Discriminative Questions	Aug 9, 2017	BenchmarkingImage Captioning	—Unverified
Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge	Aug 9, 2017	GPUVisual Question Answering	CodeCode Available
Structured Attentions for Visual Question Answering	Aug 7, 2017	Visual Question AnsweringVisual Question Answering (VQA)	CodeCode Available
Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answering	Aug 4, 2017	Question AnsweringVisual Question Answering	CodeCode Available
A Simple Loss Function for Improving the Convergence and Accuracy of Visual Question Answering Models	Aug 2, 2017	Question AnsweringVisual Question Answering	CodeCode Available
Spectral Graph-Based Method of Multimodal Word Embedding	Aug 1, 2017	Graph EmbeddingImage Retrieval	—Unverified
Improved Bilinear Pooling with CNNs	Jul 21, 2017	GPUQuestion Answering	—Unverified
Video Question Answering via Attribute-Augmented Attention Network Learning	Jul 20, 2017	AttributeInformation Retrieval	—Unverified
Visual Question Answering with Memory-Augmented Networks	Jul 17, 2017	Question AnsweringVisual Question Answering	—Unverified
Effective Approaches to Batch Parallelization for Dynamic Neural Network Architectures	Jul 8, 2017	Mixture-of-ExpertsQuestion Answering	CodeCode Available
Modulating early visual processing by language	Jul 2, 2017	Question AnsweringVisual Question Answering	CodeCode Available
Multimodal Machine Learning: Integrating Language, Vision and Speech	Jul 1, 2017	Audio-Visual Speech RecognitionBIG-bench Machine Learning	—Unverified
Are You Smarter Than a Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension	Jul 1, 2017	Question AnsweringReading Comprehension	—Unverified
Kernel Pooling for Convolutional Neural Networks	Jul 1, 2017	Face RecognitionFine-Grained Visual Categorization	—Unverified
Knowledge Acquisition for Visual Question Answering via Iterative Querying	Jul 1, 2017	Question AnsweringVisual Question Answering	—Unverified
Multi-Level Attention Networks for Visual Question Answering	Jul 1, 2017	Question AnsweringVisual Question Answering	—Unverified
A Corpus of Natural Language for Visual Reasoning	Jul 1, 2017	Question AnsweringVisual Question Answering (VQA)	—Unverified
Segmentation Guided Attention Networks for Visual Question Answering	Jul 1, 2017	Common Sense ReasoningQuestion Answering	—Unverified
Compact Tensor Pooling for Visual Question Answering	Jun 20, 2017	Question AnsweringVisual Question Answering	—Unverified
Deep learning evaluation using deep linguistic processing	Jun 5, 2017	Deep LearningMultimodal Deep Learning	—Unverified
A simple neural network module for relational reasoning	Jun 5, 2017	Image Retrieval with Multi-Modal QueryQuestion Answering	CodeCode Available
Learning Convolutional Text Representations for Visual Question Answering	May 18, 2017	General Classificationimage-classification	CodeCode Available
MUTAN: Multimodal Tucker Fusion for Visual Question Answering	May 18, 2017	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