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 121–130 of 213 papers

TitleStatusHype
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
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
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Benchmark Results

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