41 citations · 85 across the 11 of their papers we have counts for
8 papers · 1 filter
Are Object-Centric Representations Better At Compositional Generalization?
Ferdinand Kapl, Amir Mohammad Karimi Mamaghan, Maximilian Seitzer +4
Compositional generalization, the ability to reason about novel combinations of familiar concepts, is fundamental to human cognition and a critical challenge for machine learning.…
Back to the Features: DINO as a Foundation for Video World Models
Federico Baldassarre, Marc Szafraniec, Basile Terver +6
We present DINO-world, a powerful generalist video world model trained to predict future frames in the latent space of DINOv2. By leveraging a pre-trained image encoder and trainin…
CTRL-O: Language-Controllable Object-Centric Visual Representation Learning
Aniket Didolkar, Andrii Zadaianchuk, Rabiul Awal +3
Object-centric representation learning aims to decompose visual scenes into fixed-size vectors called "slots" or "object files", where each slot captures a distinct object. Current…
Temporally Consistent Object-Centric Learning by Contrasting Slots
Anna Manasyan, Maximilian Seitzer, Filip Radovic +2
Unsupervised object-centric learning from videos is a promising approach to extract structured representations from large, unlabeled collections of videos. To support downstream ta…
Zero-Shot Object-Centric Representation Learning
Aniket Didolkar, Andrii Zadaianchuk, Anirudh Goyal +4
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 objec…
DyST: Towards Dynamic Neural Scene Representations on Real-World Videos
Maximilian Seitzer, Sjoerd van Steenkiste, Thomas Kipf +2
Visual understanding of the world goes beyond the semantics and flat structure of individual images. In this work, we aim to capture both the 3D structure and dynamics of real-worl…
Object-Centric Learning for Real-World Videos by Predicting Temporal Feature Similarities
Andrii Zadaianchuk, Maximilian Seitzer, Georg Martius
Unsupervised video-based object-centric learning is a promising avenue to learn structured representations from large, unlabeled video collections, but previous approaches have onl…
Bridging the Gap to Real-World Object-Centric Learning
Maximilian Seitzer, Max Horn, Andrii Zadaianchuk +8
Humans naturally decompose their environment into entities at the appropriate level of abstraction to act in the world. Allowing machine learning algorithms to derive this decompos…