Summary of Anole: An Open, Autoregressive, Native Large Multimodal Models For Interleaved Image-text Generation, by Ethan Chern et al.
ANOLE: An Open, Autoregressive, Native Large Multimodal Models for Interleaved Image-Text Generation
by Ethan Chern, Jiadi Su, Yan Ma, Pengfei Liu
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Anole, a novel large multimodal model for interleaved image-text generation that addresses the limitations of previous models. Specifically, Anole is an autoregressive, native model that does not require adapters or separate diffusion models for visual modeling and generation. The authors build upon Meta AI’s Chameleon and employ a fine-tuning strategy that is both data-efficient and parameter-efficient. Anole demonstrates high-quality, coherent multimodal generation capabilities, and the authors have open-sourced their model, training framework, and instruction tuning data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Anole is a new way to generate images and text together. It’s special because it doesn’t need extra workarounds like other models do. The researchers took Meta AI’s Chameleon and made some changes to make it better. They used a clever approach that saves time and computer power. Anole can create realistic pictures and text combinations, and the scientists are sharing their model and tools with others. |
Keywords
» Artificial intelligence » Autoregressive » Fine tuning » Instruction tuning » Parameter efficient » Text generation
