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

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Showing 571–580 of 2167 papers

Title	Date	Tasks	Status	Hype
VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering	Aug 14, 2019	Embodied Question AnsweringQuestion Answering	CodeCode Available	1
VisualBERT: A Simple and Performant Baseline for Vision and Language	Aug 9, 2019	Language ModelingLanguage Modelling	CodeCode Available	1
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks	Aug 6, 2019	Image RetrievalQuestion Answering	CodeCode Available	1
Scene Text Visual Question Answering	May 31, 2019	Question AnsweringVisual Question Answering	CodeCode Available	1
OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge	May 31, 2019	object-detectionObject Detection	CodeCode Available	1
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering	Feb 25, 2019	Question AnsweringVisual Question Answering (VQA)	CodeCode Available	1
Faithful Multimodal Explanation for Visual Question Answering	Sep 8, 2018	Explanatory Visual Question AnsweringQuestion Answering	CodeCode Available	1
R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question Answering	May 24, 2018	Question AnsweringRelation	CodeCode Available	1
Compositional Attention Networks for Machine Reasoning	Mar 8, 2018	Referring Expression ComprehensionVisual Question Answering (VQA)	CodeCode Available	1
AI2-THOR: An Interactive 3D Environment for Visual AI	Dec 14, 2017	Deep Reinforcement LearningImitation Learning	CodeCode Available	1

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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