MetaSlot: Break Through the Fixed Number of Slots in Object-Centric Learning

Hongjia Liu; Rongzhen Zhao; Haohan Chen; Joni Pajarinen

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arXiv · 2505.20772

MetaSlot: Break Through the Fixed Number of Slots in Object-Centric Learning

Hongjia Liu·Rongzhen Zhao·Haohan Chen·Joni Pajarinen

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Abstract

Learning object-level, structured representations is widely regarded as a key to better generalization in vision and underpins the design of next-generation Pre-trained Vision Models (PVMs). Mainstream Object-Centric Learning (OCL) methods adopt Slot Attention or its variants to iteratively aggregate objects' super-pixels into a fixed set of query feature vectors, termed slots. However, their reliance on a static slot count leads to an object being represented as multiple parts when the number of objects varies. We introduce MetaSlot, a plug-and-play Slot Attention variant that adapts to variable object counts. MetaSlot (i) maintains a codebook that holds prototypes of objects in a dataset by vector-quantizing the resulting slot representations; (ii) removes duplicate slots from the traditionally aggregated slots by quantizing them with the codebook; and (iii) injects progressively weaker noise into the Slot Attention iterations to accelerate and stabilize the aggregation. MetaSlot is a general Slot Attention variant that can be seamlessly integrated into existing OCL architectures. Across multiple public datasets and tasks--including object discovery and recognition--models equipped with MetaSlot achieve significant performance gains and markedly interpretable slot representations, compared with existing Slot Attention variants.

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BibTeX RIS

Hongjia Liu, Rongzhen Zhao, Haohan Chen, Joni Pajarinen. 2025-10-08. MetaSlot: Break Through the Fixed Number of Slots in Object-Centric Learning. https://arxiv.org/abs/2505.20772

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