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 176–200 of 213 papers

TitleStatusHype
Exploring Multimodal Features and Fusion Strategies for Analyzing Disaster Tweets—0
M2D: A Multi-modal Framework for Automatic Medical Diagnosis—0
New Ideas and Trends in Deep Multimodal Content Understanding: A Review—0
Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models—0
Multimodal Deep Learning for Flaw Detection in Software Programs—0
Data-driven geophysics: from dictionary learning to deep learning—0
Multimodal Deep Unfolding for Guided Image Super-Resolution—0
A multimodal deep learning approach for named entity recognition from social media—0
Multimodal Intelligence: Representation Learning, Information Fusion, and Applications—0
Predicting the Leading Political Ideology of YouTube Channels Using Acoustic, Textual, and Metadata InformationCode0
Detecting Deception in Political Debates Using Acoustic and Textual Features—0
Multimodal Deep Learning for Mental Disorders Prediction from Audio Speech Samples—0
Multimodal Age and Gender Classification Using Ear and Profile Face ImagesCode0
Toxicity Prediction by Multimodal Deep Learning—0
Multimodal deep networks for text and image-based document classificationCode0
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
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Benchmark Results

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