41 citations · 85 across the 11 of their papers we have counts for
11 papers
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…
On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks
Maximilian Seitzer, Arash Tavakoli, Dimitrije Antic +1
Capturing aleatoric uncertainty is a critical part of many machine learning systems. In deep learning, a common approach to this end is to train a neural network to estimate the pa…
Causal Influence Detection for Improving Efficiency in Reinforcement Learning
Maximilian Seitzer, Bernhard Schölkopf, Georg Martius
Many reinforcement learning (RL) environments consist of independent entities that interact sparsely. In such environments, RL agents have only limited influence over other entitie…
Self-supervised Visual Reinforcement Learning with Object-centric Representations
Andrii Zadaianchuk, Maximilian Seitzer, Georg Martius
Autonomous agents need large repertoires of skills to act reasonably on new tasks that they have not seen before. However, acquiring these skills using only a stream of high-dimens…