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 1461–1470 of 2167 papers

Title	Date	Tasks	Status	Hype
MiniVQA - A resource to build your tailored VQA competition	Jun 1, 2021	BIG-bench Machine LearningVisual Question Answering (VQA)	—Unverified	0
CLEVR\_HYP: A Challenge Dataset and Baselines for Visual Question Answering with Hypothetical Actions over Images	Jun 1, 2021	Question AnsweringVisual Question Answering	CodeCode Available	0
MIMOQA: Multimodal Input Multimodal Output Question Answering	Jun 1, 2021	Question AnsweringVisual Question Answering	—Unverified	0
EaSe: A Diagnostic Tool for VQA based on Answer Diversity	Jun 1, 2021	DiagnosticDiversity	CodeCode Available	0
Adversarial VQA: A New Benchmark for Evaluating the Robustness of VQA Models	Jun 1, 2021	Data AugmentationQuestion Answering	—Unverified	0
LPF: A Language-Prior Feedback Objective Function for De-biased Visual Question Answering	May 29, 2021	Question AnsweringVisual Question Answering	CodeCode Available	0
StructuralLM: Structural Pre-training for Form Understanding	May 24, 2021	document-image-classificationDocument Image Classification	—Unverified	0
Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Training	May 24, 2021	Image CaptioningMedical Visual Question Answering	CodeCode Available	1
Probing Inter-modality: Visual Parsing with Self-Attention for Vision-and-Language Pre-training	May 21, 2021	Question AnsweringRelation	—Unverified	0
Multiple Meta-model Quantifying for Medical Visual Question Answering	May 19, 2021	Medical Visual Question AnsweringMeta-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