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 1481–1490 of 2167 papers

Title	Date	Tasks	Status
The Forgettable-Watcher Model for Video Question Answering	May 3, 2017	modelQuestion Answering	—Unverified
The Impact of Explanations on AI Competency Prediction in VQA	Jul 2, 2020	AI AgentLanguage Modeling	—Unverified
The meaning of "most" for visual question answering models	Dec 31, 2018	Question AnsweringVisual Question Answering	—Unverified
The Meaning of ``Most'' for Visual Question Answering Models	Aug 1, 2019	Question AnsweringVisual Question Answering	—Unverified
The Quest for Visual Understanding: A Journey Through the Evolution of Visual Question Answering	Jan 13, 2025	Common Sense ReasoningQuestion Answering	—Unverified
The VQA-Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions	Dec 16, 2016	BIG-bench Machine LearningQuestion Answering	—Unverified
The Wisdom of MaSSeS: Majority, Subjectivity, and Semantic Similarity in the Evaluation of VQA	Sep 12, 2018	Question AnsweringSemantic Similarity	—Unverified
TinyDrive: Multiscale Visual Question Answering with Selective Token Routing for Autonomous Driving	May 21, 2025	Autonomous DrivingQuestion Answering	—Unverified
TinyRS-R1: Compact Multimodal Language Model for Remote Sensing	May 17, 2025	Language ModelingLanguage Modelling	—Unverified
TinyVQA: Compact Multimodal Deep Neural Network for Visual Question Answering on Resource-Constrained Devices	Apr 4, 2024	QuantizationQuestion Answering	—Unverified

Show:10 25 50

← PrevPage 149 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