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 431–440 of 2167 papers

Title	Date	Tasks	Status	Hype
Florence: A New Foundation Model for Computer Vision	Nov 22, 2021	Action ClassificationAction Recognition	CodeCode Available	1
Many Heads but One Brain: Fusion Brain -- a Competition and a Single Multimodal Multitask Architecture	Nov 22, 2021	Handwritten Text Recognitionobject-detection	CodeCode Available	1
Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts	Nov 16, 2021	Cross-Modal RetrievalImage Captioning	CodeCode Available	1
An Empirical Study of Training End-to-End Vision-and-Language Transformers	Nov 3, 2021	Cross-Modal RetrievalDecoder	CodeCode Available	1
VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts	Nov 3, 2021	Image RetrievalImage-text Retrieval	CodeCode Available	1
ViVQA: Vietnamese Visual Question Answering	Nov 1, 2021	Question AnsweringVietnamese Visual Question Answering	CodeCode Available	1
Introspective Distillation for Robust Question Answering	Nov 1, 2021	counterfactualInductive Bias	CodeCode Available	1
IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning	Oct 25, 2021	Arithmetic ReasoningMathematical Question Answering	CodeCode Available	1
Label-Descriptive Patterns and Their Application to Characterizing Classification Errors	Oct 18, 2021	Descriptivenamed-entity-recognition	CodeCode Available	1
A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models	Oct 16, 2021	Image CaptioningLanguage Modeling	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