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 1901–1910 of 2167 papers

Title	Date	Tasks	Status	Hype
Dynamic Fusion with Intra- and Inter- Modality Attention Flow for Visual Question Answering	Dec 13, 2018	Question AnsweringVisual Question Answering	—Unverified	0
Learning Representations of Sets through Optimized Permutations	Dec 10, 2018	General ClassificationQuestion Answering	CodeCode Available	0
Spatial Knowledge Distillation to aid Visual Reasoning	Dec 10, 2018	DiagnosticKnowledge Distillation	—Unverified	0
Recursive Visual Attention in Visual Dialog	Dec 6, 2018	Question AnsweringVisual Dialog	CodeCode Available	0
Explainable and Explicit Visual Reasoning over Scene Graphs	Dec 5, 2018	Inductive BiasVisual Question Answering (VQA)	CodeCode Available	0
Learning to Compose Dynamic Tree Structures for Visual Contexts	Dec 5, 2018	Graph GenerationPanoptic Scene Graph Generation	CodeCode Available	2
Multi-task Learning of Hierarchical Vision-Language Representation	Dec 3, 2018	Multi-Task LearningQuestion Answering	—Unverified	0
Learning to Specialize with Knowledge Distillation for Visual Question Answering	Dec 1, 2018	General ClassificationGeneral Knowledge	—Unverified	0
Chain of Reasoning for Visual Question Answering	Dec 1, 2018	ObjectQuestion Answering	—Unverified	0
From Known to the Unknown: Transferring Knowledge to Answer Questions about Novel Visual and Semantic Concepts	Nov 30, 2018	Novel ConceptsQuestion Answering	—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	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