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 1391–1400 of 2167 papers

Title	Date	Tasks	Status	Hype
VQA-MHUG: A Gaze Dataset to Study Multimodal Neural Attention in Visual Question Answering	Sep 27, 2021	Question AnsweringVisual Question Answering	—Unverified	0
Multimodal Integration of Human-Like Attention in Visual Question Answering	Sep 27, 2021	Question AnsweringVisual Question Answering	—Unverified	0
How to find a good image-text embedding for remote sensing visual question answering?	Sep 24, 2021	Question AnsweringVisual Question Answering	—Unverified	0
Does Vision-and-Language Pretraining Improve Lexical Grounding?	Sep 21, 2021	Question AnsweringVisual Question Answering	CodeCode Available	1
ChipQA: No-Reference Video Quality Prediction via Space-Time Chips	Sep 17, 2021	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	1
Image Captioning for Effective Use of Language Models in Knowledge-Based Visual Question Answering	Sep 15, 2021	Image CaptioningKnowledge Graphs	CodeCode Available	0
xGQA: Cross-Lingual Visual Question Answering	Sep 13, 2021	Cross-Lingual TransferLanguage Modeling	CodeCode Available	1
Discovering the Unknown Knowns: Turning Implicit Knowledge in the Dataset into Explicit Training Examples for Visual Question Answering	Sep 13, 2021	Data AugmentationQuestion Answering	CodeCode Available	0
Towards Developing a Multilingual and Code-Mixed Visual Question Answering System by Knowledge Distillation	Sep 10, 2021	Knowledge DistillationQuestion Answering	—Unverified	0
An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA	Sep 10, 2021	Image CaptioningQuestion Answering	CodeCode Available	1

Show:10 25 50

← PrevPage 140 of 217Next →

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