Post

DreamLLM

  • Paper: DreamLLM: Synergistic Multimodal Comprehension and Creation
  • GitHub Link
  • Publisher: ICLR 2024
  • Author Affiliation: MEGVII
  • Functional Division
    • Understanding
    • Generation
  • Design Division
    • Tool-using
    • End-to-end
  • Input Modalities $\rightarrow$ Output Modalities
    (I: Image, V: Video, A: Audio, 3D: Point Cloud, T: Text, ID: Document understanding, IB: Output bounding box, IM: Output segmentation mask, IR: Output retrieved images)
    • I+T $\rightarrow$ I+T
  • Model Architecture
    (Input $\rightarrow$ Modality Encoder $\rightarrow$ Input Projector $\rightarrow$ LLM Backbone $\rightarrow$ Output Projector $\rightarrow$ Modality Generator $\rightarrow$ Output)
    • Modality Encoder
      • I: CLIP ViT-L
    • Input Projector
      • Linear Projector
    • LLM Backbone
      • Vicuna
    • Output Projector
      • MLP
    • Modality Generator
      • I: Stable Diffusion
  • Datasets Scale
    • Pre-training Stage
      • Not report
    • Instruction-tuning Stage
      • Not report
This post is licensed under CC BY 4.0 by the author.