The embedding space insight is crucial. What helped me understand this better was realizing that tokenization for images is fundamentally different than text, yet they converge to the same semantic space. Makes you wonder how this will scale once we add more modalities.
Excellent walkthrough of multimodal architecture. The key insight about converting everything to a shared embedding space is wht makes this all work - its basically creating a universal language for all modality types. I've been working on vision systems and the whole patch-based approach from ViT was a real shift from convnets. What surprised me is how simple the projection layers are considering how much heavy lifting they do in aligning those spaces. Contrastive training on image-text pairs is elegant but I dunno if people realize how much compute goes into making that alignment tight enough for practical use.
The embedding space insight is crucial. What helped me understand this better was realizing that tokenization for images is fundamentally different than text, yet they converge to the same semantic space. Makes you wonder how this will scale once we add more modalities.
Excellent walkthrough of multimodal architecture. The key insight about converting everything to a shared embedding space is wht makes this all work - its basically creating a universal language for all modality types. I've been working on vision systems and the whole patch-based approach from ViT was a real shift from convnets. What surprised me is how simple the projection layers are considering how much heavy lifting they do in aligning those spaces. Contrastive training on image-text pairs is elegant but I dunno if people realize how much compute goes into making that alignment tight enough for practical use.
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