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 26–50 of 213 papers

TitleStatusHype
Formalizing Multimedia Recommendation through Multimodal Deep LearningCode1
MoPE: Mixture of Prompt Experts for Parameter-Efficient and Scalable Multimodal FusionCode1
Learning Multimodal Data Augmentation in Feature SpaceCode1
Multimodal Emotion Recognition with Transformer-Based Self Supervised Feature FusionCode1
Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment AnalysisCode1
aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range PerceptionCode1
CLASP: Contrastive Language-Speech Pretraining for Multilingual Multimodal Information RetrievalCode1
Analysis of Social Media Data using Multimodal Deep Learning for Disaster ResponseCode1
LUMA: A Benchmark Dataset for Learning from Uncertain and Multimodal DataCode1
CardioLab: Laboratory Values Estimation and Monitoring from Electrocardiogram Signals -- A Multimodal Deep Learning ApproachCode1
Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing ApplicationsCode1
Detecting Video Game Player Burnout with the Use of Sensor Data and Machine LearningCode1
Contrastive Language-Image Pre-training for the Italian LanguageCode1
Enhancing Scene Graph Generation with Hierarchical Relationships and Commonsense KnowledgeCode1
Creation and Validation of a Chest X-Ray Dataset with Eye-tracking and Report Dictation for AI DevelopmentCode1
HYDRA: A multimodal deep learning framework for malware classificationCode1
MinkLoc++: Lidar and Monocular Image Fusion for Place RecognitionCode1
Are These Birds Similar: Learning Branched Networks for Fine-grained RepresentationsCode1
HoneyBee: A Scalable Modular Framework for Creating Multimodal Oncology Datasets with Foundational Embedding ModelsCode1
Detecting Hate Speech in Memes Using Multimodal Deep Learning Approaches: Prize-winning solution to Hateful Memes ChallengeCode1
HEALNet: Multimodal Fusion for Heterogeneous Biomedical DataCode1
Distilling Audio-Visual Knowledge by Compositional Contrastive LearningCode1
Image and Text fusion for UPMC Food-101 \ BERT and CNNsCode1
Image Search With Text Feedback by Visiolinguistic Attention LearningCode1
Medical Diagnosis with Large Scale Multimodal Transformers: Leveraging Diverse Data for More Accurate DiagnosisCode1
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Benchmark Results

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