Disentangled Representation Learning (DRL) aims to learn a model capable of identifying
and disentangling the underlying factors hidden in the observable data in representation …

Recent advances in self-supervised visual representation learning have paved the way for
unsupervised methods tackling tasks such as object discovery and instance segmentation …

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

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

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

Learning structured representations of the visual world in terms of objects promises to
significantly improve the generalization abilities of current machine learning models. While …

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

Conventional machine learning algorithms have traditionally been designed under the
assumption that input data follows a vector-based format, with an emphasis on vector-centric …

Referring image segmentation, the task of segmenting any arbitrary entities described in free-
form texts, opens up a variety of vision applications. However, manual labeling of training …

Semantic segmentation has innately relied on extensive pixel-level annotated data leading
to the emergence of unsupervised methodologies. Among them leveraging self-supervised …