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 151–175 of 213 papers

TitleStatusHype
The Role of Emotions in Informational Support Question-Response Pairs in Online Health Communities: A Multimodal Deep Learning Approach—0
Timing Is Everything: Finding the Optimal Fusion Points in Multimodal Medical Imaging—0
Toxicity Prediction by Multimodal Deep Learning—0
Unified Cross-Modal Attention-Mixer Based Structural-Functional Connectomics Fusion for Neuropsychiatric Disorder Diagnosis—0
Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models—0
Validation & Exploration of Multimodal Deep-Learning Camera-Lidar Calibration models—0
Variational methods for Conditional Multimodal Deep Learning—0
Vision-Aided Frame-Capture-Based CSI Recomposition for WiFi Sensing: A Multimodal Approach—0
Where and When: Space-Time Attention for Audio-Visual Explanations—0
Multimodal Co-learning: Challenges, Applications with Datasets, Recent Advances and Future Directions—0
Multimodal deep learning approach for joint EEG-EMG data compression and classification—0
Multimodal deep learning approach to predicting neurological recovery from coma after cardiac arrest—0
Multimodal Deep Learning-Empowered Beam Prediction in Future THz ISAC Systems—0
Multimodal Deep Learning for Finance: Integrating and Forecasting International Stock Markets—0
Multimodal Deep Learning for Flaw Detection in Software Programs—0
Multimodal Deep Learning for Low-Resource Settings: A Vector Embedding Alignment Approach for Healthcare Applications—0
Multimodal deep learning for mapping forest dominant height by fusing GEDI with earth observation data—0
Multimodal Deep Learning for Mental Disorders Prediction from Audio Speech Samples—0
Multimodal Deep Learning for Scientific Imaging Interpretation—0
Multimodal deep learning for short-term stock volatility prediction—0
Multimodal Deep Learning for Stroke Prediction and Detection using Retinal Imaging and Clinical Data—0
Multimodal Deep Learning Framework for Image Popularity Prediction on Social Media—0
Multimodal Deep Learning of Word-of-Mouth Text and Demographics to Predict Customer Rating: Handling Consumer Heterogeneity in Marketing—0
Multimodal Deep Learning to Differentiate Tumor Recurrence from Treatment Effect in Human Glioblastoma—0
Multimodal Deep Unfolding for Guided Image Super-Resolution—0
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Benchmark Results

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