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 1001–1025 of 2167 papers

Title	Date	Tasks	Status	Score
LXMERT Model Compression for Visual Question Answering	Oct 23, 2023	modelModel Compression	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
LPF: A Language-Prior Feedback Objective Function for De-biased Visual Question Answering	May 29, 2021	Question AnsweringVisual Question Answering	CodeCode Available	5
EaSe: A Diagnostic Tool for VQA based on Answer Diversity	Jun 1, 2021	DiagnosticDiversity	CodeCode Available	5
Logical Implications for Visual Question Answering Consistency	Mar 16, 2023	Language ModelingLanguage Modelling	CodeCode Available	5
Dynamic Task and Weight Prioritization Curriculum Learning for Multimodal Imagery	Oct 29, 2023	Deep LearningMultimodal Deep Learning	CodeCode Available	5
Dynamic Memory Networks for Visual and Textual Question Answering	Mar 4, 2016	Question AnsweringVisual Question Answering	CodeCode Available	5
Learning to Model and Ignore Dataset Bias with Mixed Capacity Ensembles	Nov 7, 2020	Natural Language InferenceQuestion Answering	CodeCode Available	5
Targeted Visual Prompting for Medical Visual Question Answering	Aug 6, 2024	Medical Visual Question AnsweringQuestion Answering	CodeCode Available	5
Learning to Reason: End-to-End Module Networks for Visual Question Answering	Apr 18, 2017	Visual DialogVisual Question 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
What's in a Question: Using Visual Questions as a Form of Supervision	Apr 12, 2017	Data AugmentationForm	CodeCode Available	5
MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks	Mar 29, 2023	Cross-Modal RetrievalDecoder	CodeCode Available	5
Dynamic Key-value Memory Enhanced Multi-step Graph Reasoning for Knowledge-based Visual Question Answering	Mar 6, 2022	Graph AttentionQuestion Answering	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
LLaVA-OneVision: Easy Visual Task Transfer	Aug 6, 2024	3D Question Answering (3D-QA)	CodeCode Available	5
Breaking Annotation Barriers: Generalized Video Quality Assessment via Ranking-based Self-Supervision	May 6, 2025	Learning-To-RankSelf-Supervised Learning	CodeCode Available	5
LMM-VQA: Advancing Video Quality Assessment with Large Multimodal Models	Aug 26, 2024	Large Language ModelVideo Quality Assessment	CodeCode Available	5
DVQA: Understanding Data Visualizations via Question Answering	Jan 24, 2018	ArticlesChart Question Answering	CodeCode Available	5
DualVD: An Adaptive Dual Encoding Model for Deep Visual Understanding in Visual Dialogue	Nov 17, 2019	feature selectionQuestion Answering	CodeCode Available	5
Dual Recurrent Attention Units for Visual Question Answering	Feb 1, 2018	Question AnsweringVisual Question Answering	CodeCode Available	5
Self-Bootstrapped Visual-Language Model for Knowledge Selection and Question Answering	Apr 22, 2024	Language ModelingLanguage Modelling	CodeCode Available	5
An Improved Attention for Visual Question Answering	Nov 4, 2020	DecoderQuestion Answering	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
Dual Attention Networks for Visual Reference Resolution in Visual Dialog	Feb 25, 2019	AI AgentQuestion 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