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 1131–1140 of 2167 papers

Title	Date	Tasks	Status
Improving Zero-shot Visual Question Answering via Large Language Models with Reasoning Question Prompts	Nov 15, 2023	Question AnsweringSentence	CodeCode Available
Multiple-Question Multiple-Answer Text-VQA	Nov 15, 2023	DecoderDenoising	—Unverified
Asking More Informative Questions for Grounded Retrieval	Nov 14, 2023	Question AnsweringQuestion Selection	—Unverified
CLiF-VQA: Enhancing Video Quality Assessment by Incorporating High-Level Semantic Information related to Human Feelings	Nov 13, 2023	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified
What Large Language Models Bring to Text-rich VQA?	Nov 13, 2023	Image ComprehensionOptical Character Recognition (OCR)	—Unverified
Visual Commonsense based Heterogeneous Graph Contrastive Learning	Nov 11, 2023	Contrastive LearningQuestion Answering	—Unverified
Analyzing Modular Approaches for Visual Question Decomposition	Nov 10, 2023	Code GenerationVisual Question Answering (VQA)	CodeCode Available
Improving Vision-and-Language Reasoning via Spatial Relations Modeling	Nov 9, 2023	Position regressionRelation	—Unverified
Zero-shot Translation of Attention Patterns in VQA Models to Natural Language	Nov 8, 2023	Image CaptioningLanguage Modeling	CodeCode Available
From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities	Nov 1, 2023	NavigateQuestion Answering	—Unverified

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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