title: Are We Done with Object-Centric Learning?
summary: Object-centric learning (OCL) seeks to learn representations that only encode
  an object, isolated from other objects or background cues in a scene. This
  approach underpins various aims, including out-of-distribution (OOD)
  generalization, sample-efficient composition, and modeling of structured
  environments. Most research has focused on developing unsupervised mechanisms
  that separate objects into discrete slots in the representation space,
  evaluated using unsupervised object discovery. However, with recent
  sample-efficient segmentation models, we can separate objects in the pixel
  space and encode them independently. This achieves remarkable zero-shot
  performance on OOD object discovery benchmarks, is scalable to foundation
  models, and can handle a variable number of slots out-of-the-box. Hence, the
  goal of OCL methods to obtain object-centric representations has been largely
  achieved. Despite this progress, a key question remains: How does the ability
  to separate objects within a scene contribute to broader OCL objectives, such
  as OOD generalization? We address this by investigating the OOD generalization
  challenge caused by spurious background cues through the lens of OCL. We
  propose a novel, training-free probe called Object-Centric
  Classification with Applied Masks (OCCAM), demonstrating that
  segmentation-based encoding of individual objects significantly outperforms
  slot-based OCL methods. However, challenges in real-world applications remain.
  We provide the toolbox for the OCL community to use scalable object-centric
  representations, and focus on practical applications and fundamental questions,
  such as understanding object perception in human cognition. Our code is
  available https://github.com/AlexanderRubinstein/OCCAM{here}.
paper_authors._id: 67f7826a8b50772851ccb603
paper_authors.name: Alexander Rubinstein
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-10T08:52:04.485Z
paper_authors._id: 64198d7efdfc2970b350f48f
paper_authors.avatarUrl: /avatars/c0a0f30e1cbc22f1eb6bbc4549a5709c.svg
paper_authors.fullname: Alexander Rubinstein
paper_authors.type: user
paper_authors.user: arubique
paper_authors._id: 67f7826a8b50772851ccb604
paper_authors.name: Ameya Prabhu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-10T08:52:11.053Z
paper_authors._id: 6464a0d41683d3c81f51924a
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paper_authors.fullname: Ameya Prabhu
paper_authors.type: user
paper_authors.user: AmeyaPrabhu
paper_authors._id: 67f7826a8b50772851ccb605
paper_authors.name: Matthias Bethge
paper_authors._id: 67f7826a8b50772851ccb606
paper_authors.name: Seong Joon Oh
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-10T08:52:30.797Z
paper_authors._id: 638a50450f10aa3064f03f23
paper_authors.avatarUrl: /avatars/0c068458e42950c851758a238225c3a6.svg
paper_authors.fullname: Seong Joon Oh
paper_authors.type: user
paper_authors.user: coallaoh
paper_title: Are We Done with Object-Centric Learning?
paper_summary: Object-centric learning (OCL) seeks to learn representations that only encode
  an object, isolated from other objects or background cues in a scene. This
  approach underpins various aims, including out-of-distribution (OOD)
  generalization, sample-efficient composition, and modeling of structured
  environments. Most research has focused on developing unsupervised mechanisms
  that separate objects into discrete slots in the representation space,
  evaluated using unsupervised object discovery. However, with recent
  sample-efficient segmentation models, we can separate objects in the pixel
  space and encode them independently. This achieves remarkable zero-shot
  performance on OOD object discovery benchmarks, is scalable to foundation
  models, and can handle a variable number of slots out-of-the-box. Hence, the
  goal of OCL methods to obtain object-centric representations has been largely
  achieved. Despite this progress, a key question remains: How does the ability
  to separate objects within a scene contribute to broader OCL objectives, such
  as OOD generalization? We address this by investigating the OOD generalization
  challenge caused by spurious background cues through the lens of OCL. We
  propose a novel, training-free probe called Object-Centric
  Classification with Applied Masks (OCCAM), demonstrating that
  segmentation-based encoding of individual objects significantly outperforms
  slot-based OCL methods. However, challenges in real-world applications remain.
  We provide the toolbox for the OCL community to use scalable object-centric
  representations, and focus on practical applications and fundamental questions,
  such as understanding object perception in human cognition. Our code is
  available https://github.com/AlexanderRubinstein/OCCAM{here}.
paper_ai_summary: Segmentation-based OCL methods achieve superior zero-shot OOD performance compared to slot-based methods and provide tools for practical applications.
paper_ai_keywords: object-centric learning (OCL) out-of-distribution (OOD) generalization unsupervised object discovery pixel space segmentation models slot-based OCL Object-Centric Classification with Applied Masks (OCCAM)