Summary of Grouped Discrete Representation For Object-centric Learning, by Rongzhen Zhao et al.
Grouped Discrete Representation for Object-Centric Learning
by Rongzhen Zhao, Vivienne Wang, Juho Kannala, Joni Pajarinen
First submitted to arxiv on: 4 Nov 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 paper proposes an innovative approach to Object-Centric Learning (OCL) called Grouped Discrete Representation (GDR). Traditional OCL methods reconstruct the input image or video as its Variational Autoencoder (VAE) intermediate representation, which helps suppress pixel noise and enhance object separability. However, these methods overlook attribute-level similarities and differences between features, hindering model generalization. To address this issue, GDR decomposes features into combinatorial attributes via organized channel grouping and composes them into discrete representation using tuple indexes. Experimental results demonstrate that GDR consistently improves both Transformer- and Diffusion-based OCL methods on various datasets. Furthermore, visualizations show that our GDR captures better object separability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper finds a new way to find objects in pictures or videos called Object-Centric Learning (OCL). Right now, this method is limited because it doesn’t consider the details of the features used. To solve this problem, the researchers propose a new approach called Grouped Discrete Representation (GDR). GDR takes apart the features into smaller parts and then puts them back together in a special way to help the model learn better. The results show that GDR makes both Transformer- and Diffusion-based OCL methods work better on different datasets. |
Keywords
» Artificial intelligence » Diffusion » Generalization » Transformer » Variational autoencoder
