Summary of A Simple Latent Diffusion Approach For Panoptic Segmentation and Mask Inpainting, by Wouter Van Gansbeke et al.
A Simple Latent Diffusion Approach for Panoptic Segmentation and Mask Inpainting
by Wouter Van Gansbeke, Bert De Brabandere
First submitted to arxiv on: 18 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 proposed latent diffusion approach simplifies panoptic segmentation networks by omitting complex loss functions and post-processing steps. The method consists of two steps: first, training a shallow autoencoder to project segmentation masks to latent space, and second, training a diffusion model for image-conditioned sampling in latent space. This generative approach enables the exploration of mask completion or inpainting. Experimental results on COCO and ADE20k show strong segmentation performance. Additionally, the model demonstrates adaptability to multi-tasking by introducing learnable task embeddings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier for computers to identify objects in images without needing special modules or complicated steps. It does this by using a new way of processing information called latent diffusion. This approach first uses an autoencoder to change the format of the segmentation masks, and then uses a diffusion model to allow the computer to generate new images based on the original image. This can be useful for tasks like filling in missing parts of an object or creating new objects that are similar to existing ones. The results show that this approach works well on two popular datasets. |
Keywords
* Artificial intelligence * Autoencoder * Diffusion * Diffusion model * Latent space * Mask
