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 76–100 of 213 papers

TitleStatusHype
In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing DataCode0
DeepGraviLens: a Multi-Modal Architecture for Classifying Gravitational Lensing DataCode0
MVX-ViT: Multimodal Collaborative Perception for 6G V2X Network Management Decisions Using Vision Transformer.Code0
Multimodal Guidance Network for Missing-Modality Inference in Content ModerationCode0
Multimodal Learning for Hateful Memes DetectionCode0
Multimodal Marvels of Deep Learning in Medical Diagnosis: A Comprehensive Review of COVID-19 DetectionCode0
Cultural-Aware AI Model for Emotion RecognitionCode0
Cross-Modal Attribute Insertions for Assessing the Robustness of Vision-and-Language LearningCode0
Multimodal Deep Learning for Subtype Classification in Breast Cancer Using Histopathological Images and Gene Expression DataCode0
Multimodal deep networks for text and image-based document classificationCode0
HyMNet: a Multimodal Deep Learning System for Hypertension Classification using Fundus Photographs and Cardiometabolic Risk FactorsCode0
Multimodal Deep Learning for Personalized Renal Cell Carcinoma Prognosis: Integrating CT Imaging and Clinical DataCode0
Multimodal Deep Learning for Robust RGB-D Object RecognitionCode0
An Interpretable Adaptive Multiscale Attention Deep Neural Network for Tabular DataCode0
Gaze-Guided Learning: Avoiding Shortcut Bias in Visual ClassificationCode0
Frozen Large-scale Pretrained Vision-Language Models are the Effective Foundational Backbone for Multimodal Breast Cancer PredictionCode0
Modeling of spatially embedded networks via regional spatial graph convolutional networksCode0
Focus on Focus: Focus-oriented Representation Learning and Multi-view Cross-modal Alignment for Glioma GradingCode0
Feature importance to explain multimodal prediction models. A clinical use caseCode0
A Multimodal PDE Foundation Model for Prediction and Scientific Text DescriptionsCode0
Building Multimodal AI ChatbotsCode0
Learn to Combine Modalities in Multimodal Deep LearningCode0
Multimodal Age and Gender Classification Using Ear and Profile Face ImagesCode0
EmoNets: Multimodal deep learning approaches for emotion recognition in video—0
ECC Analyzer: Extract Trading Signal from Earnings Conference Calls using Large Language Model for Stock Performance Prediction—0
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Benchmark Results

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