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 1951–1960 of 2167 papers

Title	Date	Tasks	Status
Beyond Raw Videos: Understanding Edited Videos with Large Multimodal Model	Jun 15, 2024	Question AnsweringVideo Understanding	CodeCode Available
A Question-Centric Model for Visual Question Answering in Medical Imaging	Mar 2, 2020	Medical Image AnalysisQuestion Answering	CodeCode Available
Did the Model Understand the Question?	May 14, 2018	modelQuestion Answering	CodeCode Available
Dialog without Dialog Data: Learning Visual Dialog Agents from VQA Data	Jul 24, 2020	Visual DialogVisual Question Answering (VQA)	CodeCode Available
InstructOCR: Instruction Boosting Scene Text Spotting	Dec 20, 2024	Optical Character Recognition (OCR)Text Spotting	CodeCode Available
VinVL+L: Enriching Visual Representation with Location Context in VQA	Feb 22, 2023	Question AnsweringTAG	CodeCode Available
QACE: Asking Questions to Evaluate an Image Caption	Aug 28, 2021	Question AnsweringVisual Question Answering (VQA)	CodeCode Available
Inferring and Executing Programs for Visual Reasoning	May 10, 2017	Visual Question Answering (VQA)Visual Reasoning	CodeCode Available
Incorporating Probing Signals into Multimodal Machine Translation via Visual Question-Answering Pairs	Oct 26, 2023	AttributeMachine Translation	CodeCode Available
QAVA: Query-Agnostic Visual Attack to Large Vision-Language Models	Apr 15, 2025	Question AnsweringVisual Question Answering	CodeCode Available

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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	InternVL-C	Accuracy	81.2	—	Unverified
10	Lyrics	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