Summary of Freeedit: Mask-free Reference-based Image Editing with Multi-modal Instruction, by Runze He et al.
FreeEdit: Mask-free Reference-based Image Editing with Multi-modal Instruction
by Runze He, Kai Ma, Linjiang Huang, Shaofei Huang, Jialin Gao, Xiaoming Wei, Jiao Dai, Jizhong Han, Si Liu
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed FreeEdit approach enables user-specified visual concepts in image editing by leveraging a multi-modal instruction encoder to guide the editing process. This eliminates the need for manual editing masks, and a Decoupled Residual ReferAttention (DRRA) module is introduced to reconstruct reference details. The FreeBench dataset is curated using a twice-repainting scheme, comprising images before and after editing, detailed instructions, and a reference image. FreeEdit achieves high-quality zero-shot editing through convenient language instructions, outperforming existing methods across multiple task types. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you can tell an AI exactly what you want to see in a picture, like “add a cat” or “change the color of the car.” This paper introduces a new way to do just that. They developed a system called FreeEdit that uses natural language instructions to edit images. It’s like giving a recipe to a chef, but instead of cooking food, it creates a new image based on what you want. The system is trained on a special dataset they created, which includes images before and after editing, as well as the instructions used to make the changes. This allows the AI to learn how to edit images in a way that’s both accurate and efficient. |
Keywords
» Artificial intelligence » Encoder » Multi modal » Zero shot