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 191–200 of 213 papers

TitleStatusHype
Deep Coupled-Representation Learning for Sparse Linear Inverse Problems with Side Information—0
Multimodal Deep Learning for Finance: Integrating and Forecasting International Stock Markets—0
Multimodal deep learning for short-term stock volatility prediction—0
Hybrid Attention based Multimodal Network for Spoken Language Classification—0
Correlation Net: Spatiotemporal multimodal deep learning for action recognition—0
Learn to Combine Modalities in Multimodal Deep LearningCode0
Fine-grained Video Attractiveness Prediction Using Multimodal Deep Learning on a Large Real-world Dataset—0
A Hybrid Method for Traffic Flow Forecasting Using Multimodal Deep Learning—0
AJILE Movement Prediction: Multimodal Deep Learning for Natural Human Neural Recordings and Video—0
XFlow: Cross-modal Deep Neural Networks for Audiovisual ClassificationCode0
Show:102550
← PrevPage 20 of 22Next →

Benchmark Results

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