Summary of Instruct-imagen: Image Generation with Multi-modal Instruction, by Hexiang Hu et al.
Instruct-Imagen: Image Generation with Multi-modal Instruction
by Hexiang Hu, Kelvin C.K. Chan, Yu-Chuan Su, Wenhu Chen, Yandong Li, Kihyuk Sohn, Yang Zhao, Xue Ben, Boqing Gong, William Cohen, Ming-Wei Chang, Xuhui Jia
First submitted to arxiv on: 3 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 proposes instruct-imagen, a novel approach for heterogeneous image generation tasks that generalizes well across unseen tasks. The model employs multi-modal instruction, a task representation that articulates various generation intents with precision. This enables the standardization of diverse modalities (text, edge, style, subject) into a uniform format using natural language. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a way to create different images by giving instructions, like “make a picture of a sunny day” or “draw an image of a cat.” Researchers have developed a new model that can do just that. They call it instruct-imagen, and it’s really good at creating pictures based on what you tell it to do. The team came up with something called multi-modal instruction, which is like a set of rules that helps the computer understand what kind of picture you want. |
Keywords
» Artificial intelligence » Image generation » Multi modal » Precision
