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 1631–1640 of 2167 papers

Title	Date	Tasks	Status	Hype
Cross-modal Knowledge Reasoning for Knowledge-based Visual Question Answering	Aug 31, 2020	Knowledge GraphsQuestion Answering	—Unverified	0
A Dataset and Baselines for Visual Question Answering on Art	Aug 28, 2020	Question AnsweringQuestion Generation	CodeCode Available	1
Visual Question Answering on Image Sets	Aug 27, 2020	Question AnsweringVisual Question Answering	—Unverified	0
No-Reference Video Quality Assessment Using Space-Time Chips	Aug 23, 2020	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	0
Document Visual Question Answering Challenge 2020	Aug 20, 2020	Question AnsweringRetrieval	—Unverified	0
Linguistically-aware Attention for Reducing the Semantic-Gap in Vision-Language Tasks	Aug 18, 2020	Image CaptioningVisual Question Answering (VQA)	—Unverified	0
DeVLBert: Learning Deconfounded Visio-Linguistic Representations	Aug 16, 2020	Image RetrievalQuestion Answering	CodeCode Available	1
Graph Edit Distance Reward: Learning to Edit Scene Graph	Aug 15, 2020	Graph MatchingImage Retrieval	—Unverified	0
Assisting Scene Graph Generation with Self-Supervision	Aug 8, 2020	Graph GenerationImage Captioning	—Unverified	0
TRRNet: Tiered Relation Reasoning for Compositional Visual Question Answering	Aug 1, 2020	ObjectQuestion Answering	—Unverified	0

Show:10 25 50

← PrevPage 164 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