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 1731–1740 of 2167 papers

Title	Date	Tasks	Status
Memory Augmented Neural Networks for Natural Language Processing	Sep 1, 2017	AI AgentLanguage Modeling	—Unverified
MetaVL: Transferring In-Context Learning Ability From Language Models to Vision-Language Models	Jun 2, 2023	In-Context LearningLanguage Modeling	—Unverified
MF2-MVQA: A Multi-stage Feature Fusion method for Medical Visual Question Answering	Nov 11, 2022	Medical Visual Question AnsweringQuestion Answering	—Unverified
MGA-VQA: Multi-Granularity Alignment for Visual Question Answering	Jan 25, 2022	Question AnsweringVisual Question Answering	—Unverified
MGFFD-VLM: Multi-Granularity Prompt Learning for Face Forgery Detection with VLM	Jul 16, 2025	AttributeFace Swapping	—Unverified
MIMOQA: Multimodal Input Multimodal Output Question Answering	Jun 1, 2021	Question AnsweringVisual Question Answering	—Unverified
MindBench: A Comprehensive Benchmark for Mind Map Structure Recognition and Analysis	Jul 3, 2024	PositionQuestion Answering	—Unverified
MiniVQA - A resource to build your tailored VQA competition	Jun 1, 2021	BIG-bench Machine LearningVisual Question Answering (VQA)	—Unverified
Misleading Failures of Partial-input Baselines	May 14, 2019	Natural Language InferenceVisual Question Answering (VQA)	—Unverified
Mitigating Dialogue Hallucination for Large Vision Language Models via Adversarial Instruction Tuning	Mar 15, 2024	HallucinationInstruction Following	—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