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 476–500 of 2167 papers

Title	Date	Tasks	Status	Hype
Spatially Aware Multimodal Transformers for TextVQA	Jul 23, 2020	Optical Character Recognition (OCR)Spatial Reasoning	CodeCode Available	1
An Empirical Study of Multimodal Model Merging	Apr 28, 2023	modelRetrieval	CodeCode Available	1
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering	Feb 25, 2019	Question AnsweringVisual Question Answering (VQA)	CodeCode Available	1
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization	Oct 7, 2016	General ClassificationImage Attribution	CodeCode Available	1
Faithful Multimodal Explanation for Visual Question Answering	Sep 8, 2018	Explanatory Visual Question AnsweringQuestion Answering	CodeCode Available	1
An Empirical Study of Training End-to-End Vision-and-Language Transformers	Nov 3, 2021	Cross-Modal RetrievalDecoder	CodeCode Available	1
FiLM: Visual Reasoning with a General Conditioning Layer	Sep 22, 2017	Image Retrieval with Multi-Modal QueryVisual Question Answering (VQA)	CodeCode Available	1
FaceBench: A Multi-View Multi-Level Facial Attribute VQA Dataset for Benchmarking Face Perception MLLMs	Mar 27, 2025	AttributeBenchmarking	CodeCode Available	1
Disentangling 3D Prototypical Networks For Few-Shot Concept Learning	Nov 6, 2020	3D geometry3D Object Detection	CodeCode Available	1
Answer Mining from a Pool of Images: Towards Retrieval-Based Visual Question Answering	Jun 29, 2023	Answer GenerationQuestion Answering	CodeCode Available	1
Bilateral Cross-Modality Graph Matching Attention for Feature Fusion in Visual Question Answering	Dec 14, 2021	Graph MatchingQuestion Answering	CodeCode Available	1
GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question Answering	Apr 20, 2021	Graph Neural NetworkGraph Question Answering	CodeCode Available	1
Distilled Dual-Encoder Model for Vision-Language Understanding	Dec 16, 2021	Image to textmodel	CodeCode Available	1
GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K Resolution	May 27, 2025	8kAvg	CodeCode Available	1
3DMIT: 3D Multi-modal Instruction Tuning for Scene Understanding	Jan 6, 2024	Scene UnderstandingVisual Question Answering (VQA)	CodeCode Available	1
T2Vs Meet VLMs: A Scalable Multimodal Dataset for Visual Harmfulness Recognition	Sep 29, 2024	In-Context LearningQuestion Answering	CodeCode Available	1
Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformer	Feb 18, 2021	DecoderDocument Image Classification	CodeCode Available	1
Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answering	Jul 13, 2021	NavigateQuestion Answering	CodeCode Available	1
TAP: Text-Aware Pre-training for Text-VQA and Text-Caption	Dec 8, 2020	Caption GenerationLanguage Modeling	CodeCode Available	1
Hierarchical Question-Image Co-Attention for Visual Question Answering	May 31, 2016	Visual DialogVisual Question Answering	CodeCode Available	1
TextCoT: Zoom In for Enhanced Multimodal Text-Rich Image Understanding	Apr 15, 2024	Question AnsweringVisual Question Answering (VQA)	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
DocVQA: A Dataset for VQA on Document Images	Jul 1, 2020	Question AnsweringReading Comprehension	CodeCode Available	1
Think Locally, Act Globally: Federated Learning with Local and Global Representations	Jan 6, 2020	Federated LearningRepresentation Learning	CodeCode Available	1
Bridging the Gap between 2D and 3D Visual Question Answering: A Fusion Approach for 3D VQA	Feb 24, 2024	3D Question Answering (3D-QA)Question Answering	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