Post
2274
π’ Awesome Multimodal Modeling
We introduce Awesome Multimodal Modeling, a curated repository tracing the architectural evolution of multimodal intelligenceβfrom foundational fusion to native omni-models.
πΉ Taxonomy & Evolution:
Traditional Multimodal Learning β Foundational work on representation, fusion, and alignment.
Multimodal LLMs (MLLMs) β Architectures connecting vision encoders to LLMs for understanding.
Unified Multimodal Models (UMMs) β Models unifying Understanding + Generation via Diffusion, Autoregressive, or Hybrid paradigms.
Native Multimodal Models (NMMs) β Models trained from scratch on all modalities; contrasts early vs. late fusion under scaling laws.
π‘ Key Distinction:
UMMs unify tasks via generation heads; NMMs enforce interleaving through joint pre-training.
π Explore & Contribute: https://github.com/OpenEnvision-Lab/Awesome-Multimodal-Modeling
We introduce Awesome Multimodal Modeling, a curated repository tracing the architectural evolution of multimodal intelligenceβfrom foundational fusion to native omni-models.
πΉ Taxonomy & Evolution:
Traditional Multimodal Learning β Foundational work on representation, fusion, and alignment.
Multimodal LLMs (MLLMs) β Architectures connecting vision encoders to LLMs for understanding.
Unified Multimodal Models (UMMs) β Models unifying Understanding + Generation via Diffusion, Autoregressive, or Hybrid paradigms.
Native Multimodal Models (NMMs) β Models trained from scratch on all modalities; contrasts early vs. late fusion under scaling laws.
π‘ Key Distinction:
UMMs unify tasks via generation heads; NMMs enforce interleaving through joint pre-training.
π Explore & Contribute: https://github.com/OpenEnvision-Lab/Awesome-Multimodal-Modeling