SOTAVerified

Multimodal Deep Learning

Multimodal deep learning is a type of deep learning that combines information from multiple modalities, such as text, image, audio, and video, to make more accurate and comprehensive predictions. It involves training deep neural networks on data that includes multiple types of information and using the network to make predictions based on this combined data.

One of the key challenges in multimodal deep learning is how to effectively combine information from multiple modalities. This can be done using a variety of techniques, such as fusing the features extracted from each modality, or using attention mechanisms to weight the contribution of each modality based on its importance for the task at hand.

Multimodal deep learning has many applications, including image captioning, speech recognition, natural language processing, and autonomous vehicles. By combining information from multiple modalities, multimodal deep learning can improve the accuracy and robustness of models, enabling them to perform better in real-world scenarios where multiple types of information are present.

Papers

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Showing 51–100 of 213 papers

TitleStatusHype
Creation and Validation of a Chest X-Ray Dataset with Eye-tracking and Report Dictation for AI DevelopmentCode1
MMEA: Entity Alignment for Multi-Modal Knowledge GraphsCode1
Jointly Fine-Tuning “BERT-like” Self Supervised Models to Improve Multimodal Speech Emotion RecognitionCode1
More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery ClassificationCode1
Image Search With Text Feedback by Visiolinguistic Attention LearningCode1
HYDRA: A multimodal deep learning framework for malware classificationCode1
Analysis of Social Media Data using Multimodal Deep Learning for Disaster ResponseCode1
Are These Birds Similar: Learning Branched Networks for Fine-grained RepresentationsCode1
Audio-Conditioned U-Net for Position Estimation in Full Sheet ImagesCode1
Ontology-based knowledge representation for bone disease diagnosis: a foundation for safe and sustainable medical artificial intelligence systems—0
Unified Cross-Modal Attention-Mixer Based Structural-Functional Connectomics Fusion for Neuropsychiatric Disorder Diagnosis—0
Multimodal Fusion of Glucose Monitoring and Food Imagery for Caloric Content Prediction—0
NewsNet-SDF: Stochastic Discount Factor Estimation with Pretrained Language Model News Embeddings via Adversarial Networks—0
BMMDetect: A Multimodal Deep Learning Framework for Comprehensive Biomedical Misconduct Detection—0
Multimodal Deep Learning for Stroke Prediction and Detection using Retinal Imaging and Clinical Data—0
Timing Is Everything: Finding the Optimal Fusion Points in Multimodal Medical Imaging—0
Multimodal Deep Learning-Empowered Beam Prediction in Future THz ISAC Systems—0
Multimodal Doctor-in-the-Loop: A Clinically-Guided Explainable Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer—0
A Multimodal Deep Learning Approach for White Matter Shape Prediction in Diffusion MRI Tractography—0
Integrating Vision and Location with Transformers: A Multimodal Deep Learning Framework for Medical Wound Analysis—0
Gaze-Guided Learning: Avoiding Shortcut Bias in Visual ClassificationCode0
Improving Neonatal Care: An Active Dry-Contact Electrode-based Continuous EEG Monitoring System with Seizure Detection—0
Multimodal Deep Learning for Subtype Classification in Breast Cancer Using Histopathological Images and Gene Expression DataCode0
TabulaTime: A Novel Multimodal Deep Learning Framework for Advancing Acute Coronary Syndrome Prediction through Environmental and Clinical Data Integration—0
Evolution of Data-driven Single- and Multi-Hazard Susceptibility Mapping and Emergence of Deep Learning Methods—0
ADMN: A Layer-Wise Adaptive Multimodal Network for Dynamic Input Noise and Compute Resources—0
A Multimodal PDE Foundation Model for Prediction and Scientific Text DescriptionsCode0
A Self-supervised Multimodal Deep Learning Approach to Differentiate Post-radiotherapy Progression from Pseudoprogression in Glioblastoma—0
Innovative Framework for Early Estimation of Mental Disorder Scores to Enable Timely Interventions—0
Multimodal Prescriptive Deep Learning—0
Multimodal Marvels of Deep Learning in Medical Diagnosis: A Comprehensive Review of COVID-19 DetectionCode0
Frozen Large-scale Pretrained Vision-Language Models are the Effective Foundational Backbone for Multimodal Breast Cancer PredictionCode0
Uncovering the Genetic Basis of Glioblastoma Heterogeneity through Multimodal Analysis of Whole Slide Images and RNA Sequencing DataCode0
Reducing Overtreatment of Indeterminate Thyroid Nodules Using a Multimodal Deep Learning Model—0
Validation & Exploration of Multimodal Deep-Learning Camera-Lidar Calibration models—0
Dual-Level Cross-Modal Contrastive ClusteringCode0
MVX-ViT: Multimodal Collaborative Perception for 6G V2X Network Management Decisions Using Vision Transformer.Code0
Focus on Focus: Focus-oriented Representation Learning and Multi-view Cross-modal Alignment for Glioma GradingCode0
A Systematic Review of Intermediate Fusion in Multimodal Deep Learning for Biomedical Applications—0
Audio-Visual Approach For Multimodal Concurrent Speaker Detection—0
Modeling of spatially embedded networks via regional spatial graph convolutional networksCode0
Advanced Multimodal Deep Learning Architecture for Image-Text Matching—0
Research on Optimization of Natural Language Processing Model Based on Multimodal Deep Learning—0
CarbonSense: A Multimodal Dataset and Baseline for Carbon Flux Modelling—0
Automatic Fused Multimodal Deep Learning for Plant IdentificationCode0
Multimodal Deep Learning for Low-Resource Settings: A Vector Embedding Alignment Approach for Healthcare Applications—0
Towards Precision Healthcare: Robust Fusion of Time Series and Image DataCode0
Comprehensive Multimodal Deep Learning Survival Prediction Enabled by a Transformer Architecture: A Multicenter Study in Glioblastoma—0
The Role of Emotions in Informational Support Question-Response Pairs in Online Health Communities: A Multimodal Deep Learning Approach—0
An Interpretable Adaptive Multiscale Attention Deep Neural Network for Tabular DataCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Two Branch Network (Text - Bert + Image - Nts-Net)Accuracy96.81—Unverified