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 701–710 of 2167 papers

Title	Date	Tasks	Status	Score
Multi-Page Document Visual Question Answering using Self-Attention Scoring Mechanism	Apr 29, 2024	document understandingGPU	CodeCode Available	5
Neural Module Networks	Nov 9, 2015	Visual Question AnsweringVisual Question Answering (VQA)	CodeCode Available	5
Contextual Dropout: An Efficient Sample-Dependent Dropout Module	Mar 6, 2021	image-classificationImage Classification	CodeCode Available	5
Alignment Attention by Matching Key and Query Distributions	Oct 25, 2021	Graph AttentionQuestion Answering	CodeCode Available	5
Adaptively Clustering Neighbor Elements for Image-Text Generation	Jan 5, 2023	ClusteringDecoder	CodeCode Available	5
Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations	May 15, 2019	Image CaptioningQuestion Answering	CodeCode Available	5
Multi-Image Visual Question Answering	Dec 27, 2021	Question AnsweringVisual Question Answering	CodeCode Available	5
Adaptive loose optimization for robust question answering	May 6, 2023	Extractive Question-AnsweringMachine Reading Comprehension	CodeCode Available	5
Compressing And Debiasing Vision-Language Pre-Trained Models for Visual Question Answering	Oct 26, 2022	Question AnsweringVisual Question Answering	CodeCode Available	5
MQA: Answering the Question via Robotic Manipulation	Mar 10, 2020	Imitation LearningQuestion Answering	CodeCode Available	5

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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