4 papers
MUFASA: A Multi-Layer Framework for Slot Attention
Sebastian Bock, Leonie SchüÃler, Krishnakant Singh +2
Unsupervised object-centric learning (OCL) decomposes visual scenes into distinct entities. Slot attention is a popular approach that represents individual objects as latent vector…
Evaluating Object-Centric Models beyond Object Discovery
Krishnakant Singh, Simone Schaub-Meyer, Stefan Roth
Object-centric learning (OCL) aims to learn structured scene representations that support compositional generalization and robustness to out-of-distribution (OOD) data. However, OC…
Motion-Refined DINOSAUR for Unsupervised Multi-Object Discovery
Xinrui Gong, Oliver Hahn, Christoph Reich +4
Unsupervised multi-object discovery (MOD) aims to detect and localize distinct object instances in visual scenes without any form of human supervision. Recent approaches leverage o…
GLASS: Guided Latent Slot Diffusion for Object-Centric Learning
Krishnakant Singh, Simone Schaub-Meyer, Stefan Roth
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. Ye…