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Maximilian Seitzer

10 accepted papers

2026

Are Object-Centric Representations Better At Compositional Generalization?

Ferdinand Kapl, Amir Mohammad Karimi Mamaghan, Maximilian Seitzer, Karl Johansson, Carsten Marr, Stefan Bauer +1
ICML 2026poster

Compositional generalization, the ability to reason about novel combinations of familiar concepts, is fundamental to human cognition and a critical challenge for machine learning. Object-centric (OC) representations, which encode a scene as a set of objects, are often argued to support such generali…

Cited by 0Source ↗Scholar ↗
2025

CTRL-O: Language-Controllable Object-Centric Visual Representation Learning

Aniket Didolkar, Andrii Zadaianchuk, Rabiul Awal, Maximilian Seitzer, Efstratios Gavves, Aishwarya Agrawal
CVPR 2025poster

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 state-of-the-art object-centric models have shown remarkable success in object discovery in diverse domains including c…

Cited by 2Source ↗PDF ↗Scholar ↗
2025

On the Transfer of Object-Centric Representation Learning

Aniket Rajiv Didolkar, Andrii Zadaianchuk, Anirudh Goyal, Michael Curtis Mozer, Yoshua Bengio, Georg Martius +1
ICLR 2025poster

The goal of object-centric representation learning is to decompose visual scenes into a structured representation that isolates the entities into individual vectors. Recent successes have shown that object-centric representation learning can be scaled to real-world scenes by utilizing features from…

Cited by 1Source ↗PDF ↗Scholar ↗
2025

Temporally Consistent Object-Centric Learning by Contrasting Slots

Anna Manasyan, Maximilian Seitzer, Filip Radovic, Georg Martius, Andrii Zadaianchuk
CVPR 2025poster

Unsupervised object-centric learning from videos is a promising approach to extract structured representations from large, unlabeled collections of videos. To support downstream tasks like autonomous control, these representations must be both compositional and temporally consistent. Existing approa…

Cited by 1Source ↗PDF ↗Scholar ↗
2024

DyST: Towards Dynamic Neural Scene Representations on Real-World Videos

Maximilian Seitzer, Sjoerd van Steenkiste, Thomas Kipf, Klaus Greff, Mehdi S. M. Sajjadi
ICLR 2024spotlight

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-world scenes from monocular real-world videos. Our Dynamic Scene Transformer (DyST) model leverages recent work in neural sc…

Cited by 8Source ↗PDF ↗Scholar ↗
2023

Bridging the Gap to Real-World Object-Centric Learning

Maximilian Seitzer, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun Xiao, Carl-Johann Simon-Gabriel +5
ICLR 2023poster

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 decomposition in an unsupervised way has become an important line of research. However, current methods are restricted to simula…

Cited by 144Source ↗PDF ↗Scholar ↗
2023

Object-Centric Learning for Real-World Videos by Predicting Temporal Feature Similarities

Andrii Zadaianchuk, Maximilian Seitzer, Georg Martius
NeurIPS 2023poster

Unsupervised video-based object-centric learning is a promising avenue to learn structured representations from large, unlabeled video collections, but previous approaches have only managed to scale to real-world datasets in restricted domains. Recently, it was shown that the reconstruction of pre-t…

Cited by 42Source ↗PDF ↗code ↗Scholar ↗
2022

On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks

Maximilian Seitzer, Arash Tavakoli, Dimitrije Antic, Georg Martius
ICLR 2022poster

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 parameters of a heteroscedastic Gaussian distribution by maximizing the logarithm of the likelihood function under the obs…

Cited by 131Source ↗PDF ↗code ↗Scholar ↗
2021

Causal Influence Detection for Improving Efficiency in Reinforcement Learning

Maximilian Seitzer, Bernhard Schölkopf, Georg Martius
NeurIPS 2021poster

Many reinforcement learning (RL) environments consist of independent entities that interact sparsely. In such environments, RL agents have only limited influence over other entities in any particular situation. Our idea in this work is that learning can be efficiently guided by knowing when and what…

Cited by 98Source ↗PDF ↗code ↗Scholar ↗
2021

Self-supervised Visual Reinforcement Learning with Object-centric Representations

Andrii Zadaianchuk, Maximilian Seitzer, Georg Martius
ICLR 2021spotlight

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-dimensional, unstructured, and unlabeled observations is a tricky challenge for any autonomous agent. Previous methods have us…

Cited by 54Source ↗PDF ↗code ↗Scholar ↗