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 821–830 of 2167 papers

Title	Date	Tasks	Status	Hype
Workshop on Document Intelligence Understanding	Jul 31, 2023	document understandingVisual Question Answering (VQA)	—Unverified	0
Capturing Co-existing Distortions in User-Generated Content for No-reference Video Quality Assessment	Jul 31, 2023	Action RecognitionBlocking	—Unverified	0
Bridging the Gap: Exploring the Capabilities of Bridge-Architectures for Complex Visual Reasoning Tasks	Jul 31, 2023	Image RetrievalObject	—Unverified	0
Context-VQA: Towards Context-Aware and Purposeful Visual Question Answering	Jul 28, 2023	Question AnsweringVisual Question Answering	CodeCode Available	0
BARTPhoBEiT: Pre-trained Sequence-to-Sequence and Image Transformers Models for Vietnamese Visual Question Answering	Jul 28, 2023	Question AnsweringVietnamese Visual Question Answering	—Unverified	0
Med-Flamingo: a Multimodal Medical Few-shot Learner	Jul 27, 2023	Medical Visual Question AnsweringQuestion Answering	CodeCode Available	2
Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models	Jul 26, 2023	Image Quality AssessmentNo-Reference Image Quality Assessment	CodeCode Available	1
LOIS: Looking Out of Instance Semantics for Visual Question Answering	Jul 26, 2023	Question AnsweringVisual Question Answering	—Unverified	0
Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question Answering	Jul 22, 2023	Graph Representation LearningLanguage Modeling	CodeCode Available	1
Robust Visual Question Answering: Datasets, Methods, and Future Challenges	Jul 21, 2023	Question AnsweringVisual Question Answering	—Unverified	0

Show:10 25 50

← PrevPage 83 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