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 821–830 of 2167 papers

Title	Date	Tasks	Status	Score
II-MMR: Identifying and Improving Multi-modal Multi-hop Reasoning in Visual Question Answering	Feb 16, 2024	Question AnsweringTriplet	CodeCode Available	5
ILLUME: Rationalizing Vision-Language Models through Human Interactions	Aug 17, 2022	Image CaptioningQuestion Answering	CodeCode Available	5
HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluation	May 16, 2025	BenchmarkingEthics	CodeCode Available	5
CLEAR: A Dataset for Compositional Language and Elementary Acoustic Reasoning	Nov 26, 2018	Acoustic Question AnsweringQuestion Answering	CodeCode Available	5
Enhancing Interpretability and Interactivity in Robot Manipulation: A Neurosymbolic Approach	Oct 3, 2022	Referring ExpressionRobot Manipulation	CodeCode Available	5
Filling the Image Information Gap for VQA: Prompting Large Language Models to Proactively Ask Questions	Nov 20, 2023	Question AnsweringVisual Question Answering	CodeCode Available	5
FigureQA: An Annotated Figure Dataset for Visual Reasoning	Oct 19, 2017	BIG-bench Machine LearningChart Question Answering	CodeCode Available	5
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering	May 21, 2015	Question AnsweringSentence	CodeCode Available	5
Recommending Themes for Ad Creative Design via Visual-Linguistic Representations	Jan 20, 2020	Question AnsweringRecommendation Systems	CodeCode Available	5
MIRTT: Learning Multimodal Interaction Representations from Trilinear Transformers for Visual Question Answering	Nov 1, 2021	multimodal interactionMultiple-choice	CodeCode Available	5

Show:10 25 50

← PrevPage 83 of 217Next →

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