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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1951–1975 of 2167 papers

Title	Date	Tasks	Status	Hype
Multimodal Neural Machine Translation for Low-resource Language Pairs using Synthetic Data	Jul 1, 2018	Image DescriptionMachine Translation	—Unverified	0
Pushing the Limits of Radiology with Joint Modeling of Visual and Textual Information	Jul 1, 2018	Image ClassificationMachine Translation	—Unverified	0
Connecting Language and Vision to Actions	Jul 1, 2018	Image CaptioningLanguage Modeling	—Unverified	0
Semantically Equivalent Adversarial Rules for Debugging NLP models	Jul 1, 2018	Data AugmentationQuestion Answering	CodeCode Available	0
Bridging Languages through Images with Deep Partial Canonical Correlation Analysis	Jul 1, 2018	Image DescriptionImage Retrieval	CodeCode Available	0
End-to-End Audio Visual Scene-Aware Dialog using Multimodal Attention-Based Video Features	Jun 21, 2018	Question AnsweringVideo Description	CodeCode Available	0
Learning Conditioned Graph Structures for Interpretable Visual Question Answering	Jun 19, 2018	Question AnsweringVisual Question Answering	CodeCode Available	0
Learning Visual Knowledge Memory Networks for Visual Question Answering	Jun 13, 2018	Question AnsweringVisual Question Answering	—Unverified	0
iParaphrasing: Extracting Visually Grounded Paraphrases via an Image	Jun 12, 2018	Image CaptioningQuestion Answering	CodeCode Available	0
Learning Answer Embeddings for Visual Question Answering	Jun 10, 2018	Question AnsweringTransfer Learning	—Unverified	0
Cross-Dataset Adaptation for Visual Question Answering	Jun 10, 2018	Domain AdaptationQuestion Answering	—Unverified	0
CS-VQA: Visual Question Answering with Compressively Sensed Images	Jun 8, 2018	Question AnsweringVisual Question Answering	—Unverified	0
Focal Visual-Text Attention for Visual Question Answering	Jun 5, 2018	Memex Question AnsweringQuestion Answering	CodeCode Available	0
On the Flip Side: Identifying Counterexamples in Visual Question Answering	Jun 3, 2018	Question AnsweringVisual Question Answering	—Unverified	0
Categorizing Concepts With Basic Level for Vision-to-Language	Jun 1, 2018	ClusteringImage Captioning	—Unverified	0
Stacked Latent Attention for Multimodal Reasoning	Jun 1, 2018	Image CaptioningMultimodal Reasoning	—Unverified	0
An Evaluation of Image-Based Verb Prediction Models against Human Eye-Tracking Data	Jun 1, 2018	General ClassificationQuestion Answering	—Unverified	0
Visually Guided Spatial Relation Extraction from Text	Jun 1, 2018	Activity RecognitionImage Captioning	—Unverified	0
Stacking with Auxiliary Features for Visual Question Answering	Jun 1, 2018	Common Sense ReasoningQuestion Answering	—Unverified	0
Hyperbolic Attention Networks	May 24, 2018	Machine TranslationQuestion Answering	—Unverified	0
R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question Answering	May 24, 2018	Question AnsweringRelation	CodeCode Available	1
Joint Image Captioning and Question Answering	May 22, 2018	Image CaptioningQuestion Answering	—Unverified	0
Bilinear Attention Networks	May 21, 2018	Visual Question AnsweringVisual Question Answering (VQA)	CodeCode Available	3
Reproducibility Report for "Learning To Count Objects In Natural Images For Visual Question Answering"	May 21, 2018	Question AnsweringVisual Question Answering	—Unverified	0
Did the Model Understand the Question?	May 14, 2018	modelQuestion Answering	CodeCode Available	0

Show:10 25 50

← PrevPage 79 of 87Next →

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