Summary of Instantswap: Fast Customized Concept Swapping Across Sharp Shape Differences, by Chenyang Zhu et al.
InstantSwap: Fast Customized Concept Swapping across Sharp Shape Differences
by Chenyang Zhu, Kai Li, Yue Ma, Longxiang Tang, Chengyu Fang, Chubin Chen, Qifeng Chen, Xiu Li
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper introduces a new method for Customized Concept Swapping (CCS) called InstantSwap, which addresses the challenges of inconsistency and inefficiency in previous CCS methods. The proposed approach extracts the bounding box of objects in the source image using attention maps, ensuring both foreground and background consistency during concept swapping. A cross-attention mechanism is employed to inject semantic information into both source and target concepts, promoting semantic-enhanced representations that focus on foreground objects. To improve efficiency, gradients are calculated periodically rather than at each timestep, resulting in a more efficient yet slightly less accurate approach. The paper also establishes a benchmark dataset for comprehensive evaluation, demonstrating the superiority and versatility of InstantSwap. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary InstantSwap is a new way to swap concepts between images. Imagine taking a picture of a cat, then changing it into a dog without changing anything else around the cat. This is tricky because the shape of the cat’s body might be very different from the dog’s body, making it hard to make the change look natural. To solve this problem, InstantSwap uses a clever technique called cross-attention that helps it focus on the cat and not the background. It also saves time by only calculating some things periodically rather than all the time. The result is an efficient way to swap concepts between images while still making sure it looks realistic. |
Keywords
» Artificial intelligence » Attention » Bounding box » Cross attention
