Unsupervised multi-object segmentation has shown impressive results on images by
utilizing powerful semantics learned from self-supervised pretraining. An additional modality …

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 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 …

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 choice of representation plays a key role in self-driving. Bird's eye view (BEV)
representations have shown remarkable performance in recent years. In this paper, we …

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

Recent works have shown that objects discovery can largely benefit from the inherent
motion information in video data. However, these methods lack a proper background …

Moving object detection and segmentation from a single moving camera is a challenging
task, requiring an understanding of recognition, motion and 3D geometry. Combining both …

The aim of object-centric vision is to construct an explicit representation of the objects in a
scene. This representation is obtained via a set of interchangeable modules called\emph …

We introduce a method to segment the visual field into independently moving regions,
trained with no ground truth or supervision. It consists of an adversarial conditional encoder …