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 976–1000 of 2167 papers

Title	Date	Tasks	Status	Score
Marten: Visual Question Answering with Mask Generation for Multi-modal Document Understanding	Mar 18, 2025	document understandingQuestion Answering	CodeCode Available	5
Logical Implications for Visual Question Answering Consistency	Mar 16, 2023	Language ModelingLanguage Modelling	CodeCode Available	5
Locally Smoothed Neural Networks	Nov 22, 2017	Face VerificationQuestion Answering	CodeCode Available	5
Effective Approaches to Batch Parallelization for Dynamic Neural Network Architectures	Jul 8, 2017	Mixture-of-ExpertsQuestion Answering	CodeCode Available	5
Looking Beyond Visible Cues: Implicit Video Question Answering via Dual-Clue Reasoning	Jun 9, 2025	Future predictionQuestion Answering	CodeCode Available	5
Visuo-Linguistic Question Answering (VLQA) Challenge	May 1, 2020	Question AnsweringReading Comprehension	CodeCode Available	5
Visual Question Answering: A Survey of Methods and Datasets	Jul 20, 2016	General KnowledgeSurvey	CodeCode Available	5
LLM-Assisted Multi-Teacher Continual Learning for Visual Question Answering in Robotic Surgery	Feb 26, 2024	Continual LearningExemplar-Free	CodeCode Available	5
Learning content and context with language bias for Visual Question Answering	Dec 21, 2020	Question AnsweringVisual Question Answering	CodeCode Available	5
Learning Convolutional Text Representations for Visual Question Answering	May 18, 2017	General Classificationimage-classification	CodeCode Available	5
Bridging Languages through Images with Deep Partial Canonical Correlation Analysis	Jul 1, 2018	Image DescriptionImage Retrieval	CodeCode Available	5
ECG Heartbeat Classification: A Deep Transferable Representation	Apr 19, 2018	Arrhythmia DetectionElectrocardiography (ECG)	CodeCode Available	5
Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning Model	Jul 29, 2018	Visual Question Answering (VQA)	CodeCode Available	5
BioD2C: A Dual-level Semantic Consistency Constraint Framework for Biomedical VQA	Mar 4, 2025	Medical DiagnosisQuestion Answering	CodeCode Available	5
Learning Representations of Sets through Optimized Permutations	Dec 10, 2018	General ClassificationQuestion Answering	CodeCode Available	5
LMM-VQA: Advancing Video Quality Assessment with Large Multimodal Models	Aug 26, 2024	Large Language ModelVideo Quality Assessment	CodeCode Available	5
Loss re-scaling VQA: Revisiting the LanguagePrior Problem from a Class-imbalance View	Oct 30, 2020	Face Recognitionimage-classification	CodeCode Available	5
EaSe: A Diagnostic Tool for VQA based on Answer Diversity	Jun 1, 2021	DiagnosticDiversity	CodeCode Available	5
Dynamic Task and Weight Prioritization Curriculum Learning for Multimodal Imagery	Oct 29, 2023	Deep LearningMultimodal Deep Learning	CodeCode Available	5
LININ: Logic Integrated Neural Inference Network for Explanatory Visual Question Answering	Dec 24, 2024	Explanatory Visual Question AnsweringMultimodal Reasoning	CodeCode Available	5
Dynamic Memory Networks for Visual and Textual Question Answering	Mar 4, 2016	Question AnsweringVisual Question Answering	CodeCode Available	5
Targeted Visual Prompting for Medical Visual Question Answering	Aug 6, 2024	Medical Visual Question AnsweringQuestion Answering	CodeCode Available	5
LLaVA-OneVision: Easy Visual Task Transfer	Aug 6, 2024	3D Question Answering (3D-QA)	CodeCode Available	5
Learning to Collocate Visual-Linguistic Neural Modules for Image Captioning	Oct 4, 2022	Image CaptioningSentence	CodeCode Available	5
LPF: A Language-Prior Feedback Objective Function for De-biased Visual Question Answering	May 29, 2021	Question AnsweringVisual Question Answering	CodeCode Available	5

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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