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 126–150 of 213 papers

TitleStatusHype
Multimodal Urban Areas of Interest Generation via Remote Sensing Imagery and Geographical Prior—0
Multimodal Learning To Improve Cardiac Late Mechanical Activation Detection From Cine MR Images—0
Multimodal Prescriptive Deep Learning—0
Multimodal self-supervised learning for lesion localization—0
Multi-objective optimization determines when, which and how to fuse deep networks: an application to predict COVID-19 outcomes—0
New Ideas and Trends in Deep Multimodal Content Understanding: A Review—0
NewsNet-SDF: Stochastic Discount Factor Estimation with Pretrained Language Model News Embeddings via Adversarial Networks—0
Performance Optimization using Multimodal Modeling and Heterogeneous GNN—0
Predicting Online Video Advertising Effects with Multimodal Deep Learning—0
Predicting the Skies: A Novel Model for Flight-Level Passenger Traffic Forecasting—0
Language-Assisted Deep Learning for Autistic Behaviors Recognition—0
Progress Estimation and Phase Detection for Sequential Processes—0
R2D2 at SemEval-2022 Task 5: Attention is only as good as its Values! A multimodal system for identifying misogynist memes—0
Recent Advances and Trends in Multimodal Deep Learning: A Review—0
Reducing Overtreatment of Indeterminate Thyroid Nodules Using a Multimodal Deep Learning Model—0
Research on Image Recognition Technology Based on Multimodal Deep Learning—0
Research on Optimization of Natural Language Processing Model Based on Multimodal Deep Learning—0
Scalable multimodal convolutional networks for brain tumour segmentation—0
Show me your NFT and I tell you how it will perform: Multimodal representation learning for NFT selling price prediction—0
SynthScribe: Deep Multimodal Tools for Synthesizer Sound Retrieval and Exploration—0
TabulaTime: A Novel Multimodal Deep Learning Framework for Advancing Acute Coronary Syndrome Prediction through Environmental and Clinical Data Integration—0
Temporal Multimodal Learning in Audiovisual Speech Recognition—0
TextAug: Test time Text Augmentation for Multimodal Person Re-identification—0
The Influence of Audio on Video Memorability with an Audio Gestalt Regulated Video Memorability System—0
The Role of Emotions in Informational Support Question-Response Pairs in Online Health Communities: A Multimodal Deep Learning Approach—0
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Benchmark Results

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