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 1251–1260 of 2167 papers

Title	Date	Tasks	Status
Open-Ended Visual Question Answering by Multi-Modal Domain Adaptation	Nov 11, 2019	Domain AdaptationQuestion Answering	—Unverified
OptiBox: Breaking the Limits of Proposals for Visual Grounding	Nov 29, 2019	Image CaptioningVisual Grounding	—Unverified
Optimizing Explanations by Network Canonization and Hyperparameter Search	Nov 30, 2022	Explainable Artificial Intelligence (XAI)image-classification	—Unverified
Optimizing Vision-Language Interactions Through Decoder-Only Models	Dec 14, 2024	DecoderImage Captioning	—Unverified
Optimizing Visual Question Answering Models for Driving: Bridging the Gap Between Human and Machine Attention Patterns	Jun 13, 2024	Autonomous DrivingQuestion Answering	—Unverified
ORD: Object Relationship Discovery for Visual Dialogue Generation	Jun 15, 2020	Dialogue GenerationGraph Attention	—Unverified
ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation	Mar 25, 2025	Action GenerationAutonomous Driving	—Unverified
Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering	Nov 1, 2018	Factual Visual Question AnsweringGeneral Knowledge	—Unverified
Overcoming Language Bias in Remote Sensing Visual Question Answering via Adversarial Training	Jun 1, 2023	Question AnsweringVisual Question Answering	—Unverified
Overcoming Language Priors for Visual Question Answering Based on Knowledge Distillation	Jan 10, 2025	Knowledge DistillationQuestion 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