Multimodal Deep Learning
원문 초록
This book is the result of a seminar in which we reviewed multimodal approaches and attempted to create a solid overview of the field, starting with the current state-of-the-art approaches in the two subfields of Deep Learning individually. Further, modeling frameworks are discussed where one modality is transformed into the other, as well as models in which one modality is utilized to enhance representation learning for the other. To conclude the second part, architectures with a focus on handling both modalities simultaneously are introduced. Finally, we also cover other modalities as well as general-purpose multi-modal models, which are able to handle different tasks on different modalities within one unified architecture. One interesting application (Generative Art) eventually caps off this booklet.
함께 살펴볼 논문
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Multimodal Emotion Recognition with Transformer-Based Self Supervised Feature Fusion
Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis
Dual Dimensions Geometric Representation Learning Based Document Dewarping
Feed-Forward SceneDINO for Unsupervised Semantic Scene Completion
Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations
Molecular Machine Learning Using Euler Characteristic Transforms
토론
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