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 1351–1360 of 2167 papers

Title	Date	Tasks	Status	Hype
ICDAR 2021 Competition on Document VisualQuestion Answering	Nov 10, 2021	Visual Question Answering (VQA)	—Unverified	0
Visual Question Answering based on Formal Logic	Nov 8, 2021	Formal LogicQuestion Answering	—Unverified	0
An Empirical Study of Training End-to-End Vision-and-Language Transformers	Nov 3, 2021	Cross-Modal RetrievalDecoder	CodeCode Available	1
VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts	Nov 3, 2021	Image RetrievalImage-text Retrieval	CodeCode Available	1
ViVQA: Vietnamese Visual Question Answering	Nov 1, 2021	Question AnsweringVietnamese Visual Question Answering	CodeCode Available	1
CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization	Nov 1, 2021	Answer GenerationQuestion-Answer-Generation	—Unverified	0
Diversity and Consistency: Exploring Visual Question-Answer Pair Generation	Nov 1, 2021	DiversityQuestion Answering	—Unverified	0
MIRTT: Learning Multimodal Interaction Representations from Trilinear Transformers for Visual Question Answering	Nov 1, 2021	multimodal interactionMultiple-choice	CodeCode Available	0
Introspective Distillation for Robust Question Answering	Nov 1, 2021	counterfactualInductive Bias	CodeCode Available	1
Subtleties in the trainability of quantum machine learning models	Oct 27, 2021	BIG-bench Machine LearningQuantum Machine Learning	—Unverified	0

Show:10 25 50

← PrevPage 136 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	InternVL-C	Accuracy	81.2	—	Unverified
10	Lyrics	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