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 1161–1170 of 2167 papers

Title	Date	Tasks	Status
A Corpus of Natural Language for Visual Reasoning	Jul 1, 2017	Question AnsweringVisual Question Answering (VQA)	—Unverified
Action Verb Corpus	May 1, 2018	Action ClassificationLanguage Acquisition	—Unverified
Actively Seeking and Learning from Live Data	Apr 5, 2019	Domain AdaptationMeta-Learning	—Unverified
Ada-DQA: Adaptive Diverse Quality-aware Feature Acquisition for Video Quality Assessment	Aug 1, 2023	DiversityKnowledge Distillation	—Unverified
A Dataset for Multimodal Question Answering in the Cultural Heritage Domain	Dec 1, 2016	Question AnsweringSpeech Recognition	—Unverified
A dataset of clinically generated visual questions and answers about radiology images	Nov 20, 2018	Decision MakingMedical Visual Question Answering	—Unverified
``A Distorted Skull Lies in the Bottom Center...'' Identifying Paintings from Text Descriptions	Jun 1, 2016	Question AnsweringVisual Question Answering (VQA)	—Unverified
Advancing Large Multi-modal Models with Explicit Chain-of-Reasoning and Visual Question Generation	Jan 18, 2024	Caption GenerationLanguage Modeling	—Unverified
Advancing Multimodal Medical Capabilities of Gemini	May 6, 2024	Computed Tomography (CT)image-classification	—Unverified
Advancing Surgical VQA with Scene Graph Knowledge	Dec 15, 2023	Question AnsweringVisual Question 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