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 1551–1560 of 2167 papers

Title	Date	Tasks	Status	Hype
Unshuffling Data for Improved Generalization in Visual Question Answering	Jan 1, 2021	Out-of-Distribution GeneralizationQuestion Answering	—Unverified	0
Linguistically Routing Capsule Network for Out-of-Distribution Visual Question Answering	Jan 1, 2021	Novel ConceptsQuestion Answering	—Unverified	0
Erasure for Advancing: Dynamic Self-Supervised Learning for Commonsense Reasoning	Jan 1, 2021	Question AnsweringSelf-Supervised Learning	—Unverified	0
Differentiable End-to-End Program Executor for Sample and Computationally Efficient VQA	Jan 1, 2021	Question AnsweringVisual Question Answering	—Unverified	0
Seeing is Knowing! Fact-based Visual Question Answering using Knowledge Graph Embeddings	Dec 31, 2020	Common Sense ReasoningKnowledge Graph Embeddings	—Unverified	0
Detecting Hate Speech in Multi-modal Memes	Dec 29, 2020	Binary ClassificationHate Speech Detection	CodeCode Available	1
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding	Dec 29, 2020	Document Image ClassificationDocument Layout Analysis	CodeCode Available	0
Learning content and context with language bias for Visual Question Answering	Dec 21, 2020	Question AnsweringVisual Question Answering	CodeCode Available	0
Object-Centric Diagnosis of Visual Reasoning	Dec 21, 2020	DiagnosticObject	—Unverified	0
KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA	Dec 20, 2020	Visual Question Answering (VQA)	—Unverified	0

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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