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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–125 of 213 papers

TitleStatusHype
DeepStroke: An Efficient Stroke Screening Framework for Emergency Rooms with Multimodal Adversarial Deep Learning—0
TextAug: Test time Text Augmentation for Multimodal Person Re-identification—0
DeepMMSA: A Novel Multimodal Deep Learning Method for Non-small Cell Lung Cancer Survival Analysis—0
Leveraging Audio Gestalt to Predict Media Memorability—0
Deep Learning for Technical Document Classification—0
Listen to Your Favorite Melodies with img2Mxml, Producing MusicXML from Sheet Music Image by Measure-based Multimodal Deep Learning-driven Assembly—0
Deep learning evaluation using deep linguistic processing—0
Deep Coupled-Representation Learning for Sparse Linear Inverse Problems with Side Information—0
M2D: A Multi-modal Framework for Automatic Medical Diagnosis—0
MalFake: A Multimodal Fake News Identification for Malayalam using Recurrent Neural Networks and VGG-16—0
MDL-CW: A Multimodal Deep Learning Framework With Cross Weights—0
Data-driven geophysics: from dictionary learning to deep learning—0
P-Transformer: A Prompt-based Multimodal Transformer Architecture For Medical Tabular Data—0
Correlation Net: Spatiotemporal multimodal deep learning for action recognition—0
Comprehensive Multimodal Deep Learning Survival Prediction Enabled by a Transformer Architecture: A Multicenter Study in Glioblastoma—0
The Influence of Audio on Video Memorability with an Audio Gestalt Regulated Video Memorability System—0
CarbonSense: A Multimodal Dataset and Baseline for Carbon Flux Modelling—0
BMMDetect: A Multimodal Deep Learning Framework for Comprehensive Biomedical Misconduct Detection—0
MultiCrossViT: Multimodal Vision Transformer for Schizophrenia Prediction using Structural MRI and Functional Network Connectivity Data—0
The Role of Emotions in Informational Support Question-Response Pairs in Online Health Communities: A Multimodal Deep Learning Approach—0
Multimodal Approach for Metadata Extraction from German Scientific Publications—0
Audio-Visual Approach For Multimodal Concurrent Speaker Detection—0
Multimodal Co-learning: Challenges, Applications with Datasets, Recent Advances and Future Directions—0
A survey on knowledge-enhanced multimodal learning—0
Multimodal deep learning approach for joint EEG-EMG data compression and classification—0
Show:102550
← PrevPage 5 of 9Next →

Benchmark Results

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