Summary of Slot State Space Models, by Jindong Jiang et al.
Slot State Space Models
by Jindong Jiang, Fei Deng, Gautam Singh, Minseung Lee, Sungjin Ahn
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 SlotSSMs, a novel framework for incorporating independent mechanisms into State Space Models (SSMs) to preserve or encourage separation of information. Unlike conventional SSMs, SlotSSMs maintain the state as a collection of multiple vectors called slots, with sparse interactions across slots implemented via self-attention. The proposed design is evaluated in object-centric learning, 3D visual reasoning, and long-context video understanding tasks, which involve modeling multiple objects and their long-range temporal dependencies. Experimental results show substantial performance gains over existing sequence modeling methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SlotSSMs are a new way to model sequences that helps separate information about different things happening at the same time. This is useful for problems like object recognition in videos or 3D scenes, where you need to keep track of multiple objects and their movements over time. The approach combines ideas from State Space Models (SSMs) and self-attention to create a more modular model that can handle complex sequences. |
Keywords
» Artificial intelligence » Self attention
