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 941–950 of 2167 papers

Title	Date	Tasks	Status
Assessing the Robustness of Visual Question Answering Models	Nov 30, 2019	Question AnsweringVisual Question Answering	—Unverified
Generalized Hadamard-Product Fusion Operators for Visual Question Answering	Mar 26, 2018	Neural Architecture SearchQuestion Answering	—Unverified
Generalization Differences between End-to-End and Neuro-Symbolic Vision-Language Reasoning Systems	Oct 26, 2022	Question AnsweringVisual Question Answering	—Unverified
Assessing Image Quality Issues for Real-World Problems	Mar 27, 2020	Image CaptioningQuestion Answering	—Unverified
Aligned Dual Channel Graph Convolutional Network for Visual Question Answering	Jul 1, 2020	Question AnsweringVisual Question Answering	—Unverified
LLaVA-Ultra: Large Chinese Language and Vision Assistant for Ultrasound	Oct 19, 2024	Instruction FollowingKnowledge Distillation	—Unverified
LLM4VG: Large Language Models Evaluation for Video Grounding	Dec 21, 2023	Image CaptioningVideo Grounding	—Unverified
Localizing Before Answering: A Hallucination Evaluation Benchmark for Grounded Medical Multimodal LLMs	Apr 30, 2025	HallucinationHallucination Evaluation	—Unverified
Language bias in Visual Question Answering: A Survey and Taxonomy	Nov 16, 2021	Question AnsweringVisual Question Answering	—Unverified
Gender and Racial Bias in Visual Question Answering Datasets	May 17, 2022	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