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 851–860 of 2167 papers

Title	Date	Tasks	Status
Improving Users' Mental Model with Attention-directed Counterfactual Edits	Oct 13, 2021	counterfactualQuestion Answering	—Unverified
Improving Vision-and-Language Reasoning via Spatial Relations Modeling	Nov 9, 2023	Position regressionRelation	—Unverified
Improving Visual Question Answering by Referring to Generated Paragraph Captions	Jun 14, 2019	DecoderImage Captioning	—Unverified
Improving Visual Question Answering Models through Robustness Analysis and In-Context Learning with a Chain of Basic Questions	Apr 6, 2023	In-Context LearningQuestion Answering	—Unverified
A Causal Approach to Mitigate Modality Preference Bias in Medical Visual Question Answering	May 22, 2025	counterfactualMedical Visual Question Answering	—Unverified
Improving VQA and its Explanations \\ by Comparing Competing Explanations	Jun 28, 2020	Question AnsweringVisual Question Answering	—Unverified
Incorporating External Knowledge to Answer Open-Domain Visual Questions with Dynamic Memory Networks	Dec 3, 2017	Question AnsweringVisual Question Answering	—Unverified
Cycle-Consistency for Robust Visual Question Answering	Feb 15, 2019	Question AnsweringQuestion Generation	—Unverified
Latent Image and Video Resolution Prediction using Convolutional Neural Networks	Oct 17, 2024	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified
LAVIS: A Library for Language-Vision Intelligence	Sep 15, 2022	BenchmarkingImage Captioning	—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