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 901–925 of 2167 papers

Title	Date	Tasks	Status
GeoRSMLLM: A Multimodal Large Language Model for Vision-Language Tasks in Geoscience and Remote Sensing	Mar 16, 2025	Change DetectionImage Captioning	—Unverified
ISAAQ - Mastering Textbook Questions with Pre-trained Transformers and Bottom-Up and Top-Down Attention	Nov 1, 2020	Multiple-choiceQuestion Answering	—Unverified
Geometry-Aware Video Quality Assessment for Dynamic Digital Human	Oct 24, 2023	AttributeVideo Quality Assessment	—Unverified
Evaluating and Improving Interactions with Hazy Oracles	Oct 19, 2021	Object TrackingReferring Expression	—Unverified
Deep Equilibrium Multimodal Fusion	Jun 29, 2023	Visual Question Answering (VQA)	—Unverified
Iterated learning for emergent systematicity in VQA	May 3, 2021	Question AnsweringSystematic Generalization	—Unverified
It Takes Two to Tango: Towards Theory of AI's Mind	Apr 3, 2017	AttributeQuestion Answering	—Unverified
iVQA: Inverse Visual Question Answering	Oct 10, 2017	Question AnsweringQuestion Generation	—Unverified
Jaeger: A Concatenation-Based Multi-Transformer VQA Model	Oct 11, 2023	Dimensionality Reductionmodel	—Unverified
Assisting Scene Graph Generation with Self-Supervision	Aug 8, 2020	Graph GenerationImage Captioning	—Unverified
Generative Visual Question Answering	Jul 18, 2023	Generative Visual Question AnsweringQuestion Answering	—Unverified
Joint Image Captioning and Question Answering	May 22, 2018	Image CaptioningQuestion Answering	—Unverified
Combining Knowledge Graph and LLMs for Enhanced Zero-shot Visual Question Answering	Jan 22, 2025	Knowledge GraphsQuestion Answering	—Unverified
Jointly Learning Truth-Conditional Denotations and Groundings using Parallel Attention	Apr 14, 2021	Question AnsweringVisual Question Answering	—Unverified
Assessment of Subjective and Objective Quality of Live Streaming Sports Videos	Jun 15, 2021	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified
Deep Quality Assessment of Compressed Videos: A Subjective and Objective Study	May 7, 2022	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified
JTD-UAV: MLLM-Enhanced Joint Tracking and Description Framework for Anti-UAV Systems	Jan 1, 2025	Question AnsweringVisual Question Answering	—Unverified
Generating Triples with Adversarial Networks for Scene Graph Construction	Feb 7, 2018	Attributegraph construction	—Unverified
Generating Rationales in Visual Question Answering	Apr 4, 2020	Question AnsweringVisual Question Answering	—Unverified
Assessing Visual Quality of Omnidirectional Videos	Jul 14, 2019	Visual Question Answering (VQA)	—Unverified
Ontology-based knowledge representation for bone disease diagnosis: a foundation for safe and sustainable medical artificial intelligence systems	Jun 5, 2025	DiagnosticMultimodal Deep Learning	—Unverified
KAT: A Knowledge Augmented Transformer for Vision-and-Language	Jan 16, 2022	Answer GenerationDecoder	—Unverified
Generating Natural Questions from Images for Multimodal Assistants	Nov 17, 2020	Common Sense ReasoningNatural Questions	—Unverified
DePlot: One-shot visual language reasoning by plot-to-table translation	Dec 20, 2022	Chart Question AnsweringFactual Inconsistency Detection in Chart Captioning	—Unverified
Generating Natural Language Explanations for Visual Question Answering using Scene Graphs and Visual Attention	Feb 15, 2019	Explanation GenerationLanguage Modeling	—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