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 1551–1560 of 2167 papers

Title	Date	Tasks	Status
UNITER: Learning UNiversal Image-TExt Representations	Sep 25, 2019	Image-text matchingImage-text Retrieval	—Unverified
Un jeu de données pour répondre à des questions visuelles à propos d’entités nommées en utilisant des bases de connaissances (ViQuAE, a Dataset for Knowledge-based Visual Question Answering about Named Entities)	Jun 1, 2022	Question AnsweringVisual Question Answering	—Unverified
Unleashing the Potential of Large Language Model: Zero-shot VQA for Flood Disaster Scenario	Dec 4, 2023	Language ModelingLanguage Modelling	—Unverified
Unshuffling Data for Improved Generalization	Feb 27, 2020	ClusteringData Augmentation	—Unverified
Unshuffling Data for Improved Generalization in Visual Question Answering	Jan 1, 2021	Out-of-Distribution GeneralizationQuestion Answering	—Unverified
Unsupervised Keyword Extraction for Full-sentence VQA	Nov 23, 2019	Keyword ExtractionQuestion Answering	—Unverified
Unsupervised Vision-and-Language Pre-training via Retrieval-based Multi-Granular Alignment	Mar 1, 2022	RetrievalSentence	—Unverified
Unveiling Cross Modality Bias in Visual Question Answering: A Causal View with Possible Worlds VQA	May 31, 2023	counterfactualCounterfactual Inference	—Unverified
UPME: An Unsupervised Peer Review Framework for Multimodal Large Language Model Evaluation	Mar 19, 2025	Language Model EvaluationLanguage Modeling	—Unverified
Using Visual Cropping to Enhance Fine-Detail Question Answering of BLIP-Family Models	May 31, 2023	Question AnsweringVisual Question 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