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 461–470 of 2167 papers

Title	Date	Tasks	Status	Hype
REVIVE: Regional Visual Representation Matters in Knowledge-Based Visual Question Answering	Jun 2, 2022	Question AnsweringRetrieval	CodeCode Available	1
Describe Anything Model for Visual Question Answering on Text-rich Images	Jul 16, 2025	DescriptiveLanguage Modeling	CodeCode Available	1
Disentangling 3D Prototypical Networks For Few-Shot Concept Learning	Nov 6, 2020	3D geometry3D Object Detection	CodeCode Available	1
3DMIT: 3D Multi-modal Instruction Tuning for Scene Understanding	Jan 6, 2024	Scene UnderstandingVisual Question Answering (VQA)	CodeCode Available	1
R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question Answering	May 24, 2018	Question AnsweringRelation	CodeCode Available	1
Detecting and Preventing Hallucinations in Large Vision Language Models	Aug 11, 2023	16kHallucination	CodeCode Available	1
Detecting Hate Speech in Multi-modal Memes	Dec 29, 2020	Binary ClassificationHate Speech Detection	CodeCode Available	1
An Empirical Study of End-to-End Video-Language Transformers with Masked Visual Modeling	Sep 4, 2022	Fill MaskOptical Flow Estimation	CodeCode Available	1
DeVLBert: Learning Deconfounded Visio-Linguistic Representations	Aug 16, 2020	Image RetrievalQuestion Answering	CodeCode Available	1
Generalizing from SIMPLE to HARD Visual Reasoning: Can We Mitigate Modality Imbalance in VLMs?	Jan 5, 2025	Image CaptioningImage to text	CodeCode Available	1

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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