The recent success of transformer-based image generative models in object-centric learning
highlights the importance of powerful image generators for handling complex scenes …

Object-centric learning aims to represent visual data with a set of object entities (aka slots),
providing structured representations that enable systematic generalization. Leveraging …

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 …

Unsupervised object-centric learning aims to decompose scenes into interpretable object
entities termed slots. Slot-based auto-encoders stand out as a prominent method for this …

In this paper, we propose a simple yet effective approach for self-supervised video object
segmentation (VOS). Previous self-supervised VOS techniques majorly resort to auxiliary …

The ability to distill object-centric abstractions from intricate visual scenes underpins human-
level generalization. Despite the significant progress in object-centric learning methods …

Instance segmentation demands substantial labeling resources. This has prompted
increased interest to explore the object discovery task as an unsupervised alternative. In …

Compositional representations are thought to enable humans to generalize across
combinatorially vast state spaces. Models with learnable object slots, which encode …

Unsupervised object-centric learning from videos is a promising approach towards learning
compositional representations that can be applied to various downstream tasks, such as …

While modern best practices advocate for scalable architectures that support long-range
interactions, object-centric models are yet to fully embrace these architectures. In particular …