Post
2015
๐ข Awesome Multimodal Modeling
We introduce Awesome Multimodal Modeling, a curated repository tracing the architectural evolution of multimodal intelligenceโfrom foundational fusion to native omni-models.
๐น Taxonomy & Evolution:
Traditional Multimodal Learning โ Foundational work on representation, fusion, and alignment.
Multimodal LLMs (MLLMs) โ Architectures connecting vision encoders to LLMs for understanding.
Unified Multimodal Models (UMMs) โ Models unifying Understanding + Generation via Diffusion, Autoregressive, or Hybrid paradigms.
Native Multimodal Models (NMMs) โ Models trained from scratch on all modalities; contrasts early vs. late fusion under scaling laws.
๐ก Key Distinction:
UMMs unify tasks via generation heads; NMMs enforce interleaving through joint pre-training.
๐ Explore & Contribute: https://github.com/OpenEnvision-Lab/Awesome-Multimodal-Modeling
We introduce Awesome Multimodal Modeling, a curated repository tracing the architectural evolution of multimodal intelligenceโfrom foundational fusion to native omni-models.
๐น Taxonomy & Evolution:
Traditional Multimodal Learning โ Foundational work on representation, fusion, and alignment.
Multimodal LLMs (MLLMs) โ Architectures connecting vision encoders to LLMs for understanding.
Unified Multimodal Models (UMMs) โ Models unifying Understanding + Generation via Diffusion, Autoregressive, or Hybrid paradigms.
Native Multimodal Models (NMMs) โ Models trained from scratch on all modalities; contrasts early vs. late fusion under scaling laws.
๐ก Key Distinction:
UMMs unify tasks via generation heads; NMMs enforce interleaving through joint pre-training.
๐ Explore & Contribute: https://github.com/OpenEnvision-Lab/Awesome-Multimodal-Modeling
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