Summary of Slot Structured World Models, by Jonathan Collu et al.
Slot Structured World Models
by Jonathan Collu, Riccardo Majellaro, Aske Plaat, Thomas M. Moerland
First submitted to arxiv on: 8 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 This research proposes a novel approach to building intelligent artificial systems that can perceive and reason about individual objects and their interactions. The authors introduce Slot Structured World Models (SSWM), which combine an object-centric encoder based on Slot Attention with a latent graph-based dynamics model. This architecture allows for the extraction of robust, object-centric representations that can disentangle multiple objects with similar appearance. The paper evaluates SSWM in the Spriteworld benchmark, demonstrating its ability to consistently outperform baselines in multi-step prediction tasks with action-conditional object interactions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is all about making robots and computers smarter! It wants to teach them how to see individual things and understand how they interact with each other. Right now, these systems are not very good at this because they can’t tell apart different objects that look similar. The scientists propose a new way of doing things called Slot Structured World Models (SSWM). This method uses a special kind of attention to focus on specific objects and then models how those objects move and interact with each other. They tested it in a game-like environment called Spriteworld and found that it worked much better than previous methods. |
Keywords
* Artificial intelligence * Attention * Encoder
