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About this paper
Ye Zhu, Yu Wu, Nicu Sebe, Yan Yan
A comprehensive survey of multimodal machine learning, centered on computer vision, analyzes how intrinsic data characteristics across modalities like vision, audio, and text influence methodological designs. The work reviews advancements in representation learning and applications, highlighting the field's evolution towards large-scale pre-training with models like Transformers.
Key insights
- The intrinsic nature and characteristics of data modalities profoundly influence the selection and effectiveness of methodological designs.
- Multimodal representation learning has transitioned from supervised methods to large-scale non-supervised pre-training, heavily leveraging Transformer architectures.
- Generative models, particularly Diffusion Probabilistic Models (DPMs), have become foundational for cross-modality synthesis tasks like text-to-image generation.
Summary by alphaXiv.
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