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

Unsupervised video-based object-centric learning is a promising avenue to learn structured
representations from large, unlabeled video collections, but previous approaches have only …

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 …

Object-centric (OC) representations, which represent the state of a visual scene by modeling
it as a composition of objects, have the potential to be used in various downstream tasks to …

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 …

The recent enthusiasm for open-world vision systems show the high interest of the
community to perform perception tasks outside of the closed-vocabulary benchmark setups …

Guidance serves as a key concept in diffusion models, yet its effectiveness is often limited by
the need for extra data annotation or classifier pretraining. That is why guidance was …

While diffusion models have significantly advanced the quality of image generation, their
capability to accurately and coherently render text within these images remains a substantial …

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

In this paper, we address the challenge of multi-object tracking (MOT) in moving Unmanned
Aerial Vehicle (UAV) scenarios, where irregular flight trajectories, such as hovering, turning …