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 1401–1410 of 2167 papers

Title	Date	Tasks	Status
Sentence Attention Blocks for Answer Grounding	Sep 20, 2023	Question AnsweringSentence	—Unverified
Is the House Ready For Sleeptime? Generating and Evaluating Situational Queries for Embodied Question Answering	May 8, 2024	2kEmbodied Question Answering	—Unverified
Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures	May 10, 2022	AutoMLBIG-bench Machine Learning	—Unverified
Sheffield MultiMT: Using Object Posterior Predictions for Multimodal Machine Translation	Sep 1, 2017	Image CaptioningImage Classification	—Unverified
Show Why the Answer is Correct! Towards Explainable AI using Compositional Temporal Attention	May 15, 2021	Question AnsweringVisual Question Answering	—Unverified
Silence is Not Consensus: Disrupting Agreement Bias in Multi-Agent LLMs via Catfish Agent for Clinical Decision Making	May 27, 2025	Decision MakingDiagnostic	—Unverified
Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps	Dec 9, 2020	DecoderImage Captioning	—Unverified
SimpleLLM4AD: An End-to-End Vision-Language Model with Graph Visual Question Answering for Autonomous Driving	Jul 31, 2024	Autonomous DrivingLanguage Modeling	—Unverified
SimpsonsVQA: Enhancing Inquiry-Based Learning with a Tailored Dataset	Oct 30, 2024	Question AnsweringVisual Question Answering	—Unverified
SimVQA: Exploring Simulated Environments for Visual Question Answering	Mar 31, 2022	Data AugmentationDiversity	—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