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Fengyuan Liu

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AI & ML interests

Generative Modeling

Recent Activity

authored a paper about 2 months ago
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria
repliedto Juanxi's post about 2 months ago
📢 Awesome Multimodal Modeling We introduce Awesome Multimodal Modeling, a curated repository tracing the architectural evolution of multimodal intelligence—from foundational fusion to native omni-models. 🔹 Taxonomy & Evolution: Traditional Multimodal Learning – Foundational work on representation, fusion, and alignment. Multimodal LLMs (MLLMs) – Architectures connecting vision encoders to LLMs for understanding. Unified Multimodal Models (UMMs) – Models unifying Understanding + Generation via Diffusion, Autoregressive, or Hybrid paradigms. Native Multimodal Models (NMMs) – Models trained from scratch on all modalities; contrasts early vs. late fusion under scaling laws. 💡 Key Distinction: UMMs unify tasks via generation heads; NMMs enforce interleaving through joint pre-training. 🔗 Explore & Contribute: https://github.com/OpenEnvision/Awesome-Multimodal-Modeling
reacted to Juanxi's post with 😎 about 2 months ago
📢 Awesome Multimodal Modeling We introduce Awesome Multimodal Modeling, a curated repository tracing the architectural evolution of multimodal intelligence—from foundational fusion to native omni-models. 🔹 Taxonomy & Evolution: Traditional Multimodal Learning – Foundational work on representation, fusion, and alignment. Multimodal LLMs (MLLMs) – Architectures connecting vision encoders to LLMs for understanding. Unified Multimodal Models (UMMs) – Models unifying Understanding + Generation via Diffusion, Autoregressive, or Hybrid paradigms. Native Multimodal Models (NMMs) – Models trained from scratch on all modalities; contrasts early vs. late fusion under scaling laws. 💡 Key Distinction: UMMs unify tasks via generation heads; NMMs enforce interleaving through joint pre-training. 🔗 Explore & Contribute: https://github.com/OpenEnvision/Awesome-Multimodal-Modeling
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