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 711–720 of 2167 papers

Title	Date	Tasks	Status
Curriculum Learning for Compositional Visual Reasoning	Mar 27, 2023	Question AnsweringVisual Question Answering	—Unverified
Curriculum Learning Effectively Improves Low Data VQA	Dec 1, 2021	Question AnsweringVisual Question Answering	—Unverified
A Vision Centric Remote Sensing Benchmark	Mar 20, 2025	Question AnsweringRepresentation Learning	—Unverified
CT-Agent: A Multimodal-LLM Agent for 3D CT Radiology Question Answering	May 22, 2025	Computed Tomography (CT)Question Answering	—Unverified
AVIS: Autonomous Visual Information Seeking with Large Language Model Agent	Jun 13, 2023	Decision MakingLanguage Modeling	—Unverified
CS-VQA: Visual Question Answering with Compressively Sensed Images	Jun 8, 2018	Question AnsweringVisual Question Answering	—Unverified
CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization	Nov 1, 2021	Answer GenerationQuestion-Answer-Generation	—Unverified
Auto-Parsing Network for Image Captioning and Visual Question Answering	Aug 24, 2021	Image CaptioningQuestion Answering	—Unverified
A Multimodal Memes Classification: A Survey and Open Research Issues	Sep 17, 2020	ClassificationGeneral Classification	—Unverified
A dataset of clinically generated visual questions and answers about radiology images	Nov 20, 2018	Decision MakingMedical Visual 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