mlsanityData to 7 Oct 2026

GLASS: Guided Latent Slot Diffusion for Object-Centric Learning

arXiv 2407.17929 ↗First posted 25 Jul 2024cs.CV cs.LGcited by 14

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Abstract (arXiv)

Object-centric learning aims to decompose an input image into a set of meaningful object files (slots). These latent object representations enable a variety of downstream tasks. Yet, object-centric learning struggles on real-world datasets, which contain multiple objects of complex textures and shapes in natural everyday scenes. To address this, we introduce Guided Latent Slot Diffusion (GLASS), a novel slot-attention model that learns in the space of generated images and uses semantic and instance guidance modules to learn better slot embeddings for various downstream tasks. Our experiments show that GLASS surpasses state-of-the-art slot-attention methods by a wide margin on tasks such as (zero-shot) object discovery and conditional image generation for real-world scenes. Moreover, GLASS enables the first application of slot attention to the compositional generation of complex, realistic scenes.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 82 of its 106 references that are in the map. First posted on arXiv 25 Jul 2024.

Builds on

82 of 106 references.

References · 82

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 9.

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  1. 2025Slot-Guided Adaptation of Pre-trained Diffusion Models for Object-Centric Learning and Compositional Generationcited by 13
  2. 2025CTRL-O: Language-Controllable Object-Centric Visual Representation Learningcited by 12
  3. 2025Motion-Refined DINOSAUR for Unsupervised Multi-Object Discoverycited by 4
  4. 2026Improved Object-Centric Diffusion Learning with Registers and Contrastive Alignmentcited by 3
  5. 2026Unifying Object-Centric World Models and Diffusion Policy: A Hierarchical Framework for Multi-Stage Robotic Taskscited by 2
  6. 2026MUFASA: A Multi-Layer Framework for Slot Attentioncited by 1
  7. 2025Compositional Video Synthesis by Temporal Object-Centric Learningcited by 0
  8. 2025Unsupervised Structural Scene Decomposition via Foreground-Aware Slot Attention with Pseudo-Mask Guidancecited by 0
  9. 2026QASA: Quality-Guided K-Adaptive Slot Attention for Unsupervised Object-Centric Learningcited by 0
  10. 2026Evaluating Object-Centric Models beyond Object Discoverycited by 0
  11. 2026When Slots Compete: Slot Merging in Object-Centric Learningcited by 0
  12. 2026Deep Sprite-based Image Models: An Analysiscited by 0
2 more
  1. 2026CenSynCMB: Centre Maps and Physics-Guided Synthesis for Microbleed Detectioncited by 0
  2. 2026Slot-RAE: Streamlining Object-Centric Learning via Direct Representation Auto-Encoderscited by 0