---
_id: '14218'
abstract:
- lang: eng
  text: 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 simulated data or require
    additional information in the form of motion or depth in order to successfully
    discover objects. In this work, we overcome this limitation by showing that reconstructing
    features from models trained in a self-supervised manner is a sufficient training
    signal for object-centric representations to arise in a fully unsupervised way.
    Our approach, DINOSAUR, significantly out-performs existing image-based object-centric
    learning models on simulated data and is the first unsupervised object-centric
    model that scales to real-world datasets such as COCO and PASCAL VOC. DINOSAUR
    is conceptually simple and shows competitive performance compared to more involved
    pipelines from the computer vision literature.
article_processing_charge: No
arxiv: 1
author:
- first_name: Maximilian
  full_name: Seitzer, Maximilian
  last_name: Seitzer
- first_name: Max
  full_name: Horn, Max
  last_name: Horn
- first_name: Andrii
  full_name: Zadaianchuk, Andrii
  last_name: Zadaianchuk
- first_name: Dominik
  full_name: Zietlow, Dominik
  last_name: Zietlow
- first_name: Tianjun
  full_name: Xiao, Tianjun
  last_name: Xiao
- first_name: Carl-Johann Simon-Gabriel
  full_name: Carl-Johann Simon-Gabriel, Carl-Johann Simon-Gabriel
  last_name: Carl-Johann Simon-Gabriel
- first_name: Tong
  full_name: He, Tong
  last_name: He
- first_name: Zheng
  full_name: Zhang, Zheng
  last_name: Zhang
- first_name: Bernhard
  full_name: Schölkopf, Bernhard
  last_name: Schölkopf
- first_name: Thomas
  full_name: Brox, Thomas
  last_name: Brox
- first_name: Francesco
  full_name: Locatello, Francesco
  id: 26cfd52f-2483-11ee-8040-88983bcc06d4
  last_name: Locatello
  orcid: 0000-0002-4850-0683
citation:
  ama: 'Seitzer M, Horn M, Zadaianchuk A, et al. Bridging the gap to real-world object-centric
    learning. In: <i>The 11th International Conference on Learning Representations</i>.
    ; 2023.'
  apa: Seitzer, M., Horn, M., Zadaianchuk, A., Zietlow, D., Xiao, T., Carl-Johann
    Simon-Gabriel, C.-J. S.-G., … Locatello, F. (2023). Bridging the gap to real-world
    object-centric learning. In <i>The 11th International Conference on Learning Representations</i>.
    Kigali, Rwanda.
  chicago: Seitzer, Maximilian, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun
    Xiao, Carl-Johann Simon-Gabriel Carl-Johann Simon-Gabriel, Tong He, et al. “Bridging
    the Gap to Real-World Object-Centric Learning.” In <i>The 11th International Conference
    on Learning Representations</i>, 2023.
  ieee: M. Seitzer <i>et al.</i>, “Bridging the gap to real-world object-centric learning,”
    in <i>The 11th International Conference on Learning Representations</i>, Kigali,
    Rwanda, 2023.
  ista: 'Seitzer M, Horn M, Zadaianchuk A, Zietlow D, Xiao T, Carl-Johann Simon-Gabriel
    C-JS-G, He T, Zhang Z, Schölkopf B, Brox T, Locatello F. 2023. Bridging the gap
    to real-world object-centric learning. The 11th International Conference on Learning
    Representations. ICLR: International Conference on Learning Representations.'
  mla: Seitzer, Maximilian, et al. “Bridging the Gap to Real-World Object-Centric
    Learning.” <i>The 11th International Conference on Learning Representations</i>,
    2023.
  short: M. Seitzer, M. Horn, A. Zadaianchuk, D. Zietlow, T. Xiao, C.-J.S.-G. Carl-Johann
    Simon-Gabriel, T. He, Z. Zhang, B. Schölkopf, T. Brox, F. Locatello, in:, The
    11th International Conference on Learning Representations, 2023.
conference:
  end_date: 2023-05-05
  location: Kigali, Rwanda
  name: 'ICLR: International Conference on Learning Representations'
  start_date: 2023-05-01
date_created: 2023-08-22T14:22:41Z
date_published: 2023-05-10T00:00:00Z
date_updated: 2024-10-14T12:30:54Z
day: '10'
department:
- _id: FrLo
extern: '1'
external_id:
  arxiv:
  - '2209.14860'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/2209.14860
month: '05'
oa: 1
oa_version: Preprint
publication: The 11th International Conference on Learning Representations
publication_status: published
quality_controlled: '1'
status: public
title: Bridging the gap to real-world object-centric learning
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...