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 511–520 of 2167 papers

Title	Date	Tasks	Status	Hype
Point and Ask: Incorporating Pointing into Visual Question Answering	Nov 27, 2020	Question AnsweringVisual Question Answering	CodeCode Available	1
Transformation Driven Visual Reasoning	Nov 26, 2020	AttributeTriplet	CodeCode Available	1
Large Scale Multimodal Classification Using an Ensemble of Transformer Models and Co-Attention	Nov 23, 2020	ClassificationGeneral Classification	CodeCode Available	1
LRTA: A Transparent Neural-Symbolic Reasoning Framework with Modular Supervision for Visual Question Answering	Nov 21, 2020	Answer GenerationQuestion Answering	CodeCode Available	1
Disentangling 3D Prototypical Networks For Few-Shot Concept Learning	Nov 6, 2020	3D geometry3D Object Detection	CodeCode Available	1
ConceptBert: Concept-Aware Representation for Visual Question Answering	Nov 1, 2020	Common Sense ReasoningQuestion Answering	CodeCode Available	1
Learning to Contrast the Counterfactual Samples for Robust Visual Question Answering	Nov 1, 2020	Contrastive Learningcounterfactual	CodeCode Available	1
MMFT-BERT: Multimodal Fusion Transformer with BERT Encodings for Visual Question Answering	Oct 27, 2020	DiagnosticQuestion Answering	CodeCode Available	1
ST-GREED: Space-Time Generalized Entropic Differences for Frame Rate Dependent Video Quality Prediction	Oct 26, 2020	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	1
RUArt: A Novel Text-Centered Solution for Text-Based Visual Question Answering	Oct 24, 2020	Optical Character RecognitionOptical Character Recognition (OCR)	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