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Multimodal Deep Learning
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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.
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Unified Cross-Modal Attention-Mixer Based Structural-Functional Connectomics Fusion for Neuropsychiatric Disorder Diagnosis
Multimodal Fusion of Glucose Monitoring and Food Imagery for Caloric Content Prediction
NewsNet-SDF: Stochastic Discount Factor Estimation with Pretrained Language Model News Embeddings via Adversarial Networks
BMMDetect: A Multimodal Deep Learning Framework for Comprehensive Biomedical Misconduct Detection
Multimodal Deep Learning-Empowered Beam Prediction in Future THz ISAC Systems
Timing Is Everything: Finding the Optimal Fusion Points in Multimodal Medical Imaging
Multimodal Deep Learning for Stroke Prediction and Detection using Retinal Imaging and Clinical Data
Multimodal Doctor-in-the-Loop: A Clinically-Guided Explainable Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer
A Multimodal Deep Learning Approach for White Matter Shape Prediction in Diffusion MRI Tractography
Integrating Vision and Location with Transformers: A Multimodal Deep Learning Framework for Medical Wound Analysis
Gaze-Guided Learning: Avoiding Shortcut Bias in Visual Classification
Improving Neonatal Care: An Active Dry-Contact Electrode-based Continuous EEG Monitoring System with Seizure Detection
Multimodal Deep Learning for Subtype Classification in Breast Cancer Using Histopathological Images and Gene Expression Data
TabulaTime: A Novel Multimodal Deep Learning Framework for Advancing Acute Coronary Syndrome Prediction through Environmental and Clinical Data Integration
Evolution of Data-driven Single- and Multi-Hazard Susceptibility Mapping and Emergence of Deep Learning Methods
ADMN: A Layer-Wise Adaptive Multimodal Network for Dynamic Input Noise and Compute Resources
A Multimodal PDE Foundation Model for Prediction and Scientific Text Descriptions
Innovative Framework for Early Estimation of Mental Disorder Scores to Enable Timely Interventions
A Self-supervised Multimodal Deep Learning Approach to Differentiate Post-radiotherapy Progression from Pseudoprogression in Glioblastoma
Multimodal Prescriptive Deep Learning
Multimodal Marvels of Deep Learning in Medical Diagnosis: A Comprehensive Review of COVID-19 Detection
CLASP: Contrastive Language-Speech Pretraining for Multilingual Multimodal Information Retrieval
CardioLab: Laboratory Values Estimation and Monitoring from Electrocardiogram Signals -- A Multimodal Deep Learning Approach
Uncovering the Genetic Basis of Glioblastoma Heterogeneity through Multimodal Analysis of Whole Slide Images and RNA Sequencing Data
Reducing Overtreatment of Indeterminate Thyroid Nodules Using a Multimodal Deep Learning Model
Validation & Exploration of Multimodal Deep-Learning Camera-Lidar Calibration models
PHemoNet: A Multimodal Network for Physiological Signals
Dual-Level Cross-Modal Contrastive Clustering
Focus on Focus: Focus-oriented Representation Learning and Multi-view Cross-modal Alignment for Glioma Grading
A Systematic Review of Intermediate Fusion in Multimodal Deep Learning for Biomedical Applications
Audio-Visual Approach For Multimodal Concurrent Speaker Detection
LUMA: A Benchmark Dataset for Learning from Uncertain and Multimodal Data
Research on Optimization of Natural Language Processing Model Based on Multimodal Deep Learning
Advanced Multimodal Deep Learning Architecture for Image-Text Matching
CarbonSense: A Multimodal Dataset and Baseline for Carbon Flux Modelling
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