The goal of object-centric representation learning is to decompose visual scenes into a
structured representation that isolates the entities. Recent successes have shown that object …

Autoregressive models, such as the GPT family, use a fixed order, usually left-to-right, to
generate sequences. However, this is not a necessity. In this paper, we challenge this …

It has long been known in both neuroscience and AI that``binding''between neurons leads to
a form of competitive learning where representations are compressed in order to represent …

Object-centric learning (OCL) aims to learn representations of individual objects within
visual scenes without manual supervision, facilitating efficient and effective visual reasoning …

Predicting future dynamics is crucial for applications like autonomous driving and robotics,
where understanding the environment is key. Existing pixel-level methods are …

Unsupervised object-centric learning from videos is a promising approach to extract
structured representations from large, unlabeled collections of videos. To support …