  Similar to humans perceiving visual scenes as objects, Object-Centric
Learning (OCL) can abstract dense images or videos into sparse object-level
features. Transformer-based OCL handles complex textures well due to the
decoding guidance of discrete representation, obtained by discretizing noisy
features in image or video feature maps using template features from a
codebook. However, treating features as minimal units overlooks their composing
attributes, thus impeding model generalization; indexing features with natural
numbers loses attribute-level commonalities and characteristics, thus
diminishing heuristics for model convergence. We propose \textit{Grouped
Discrete Representation} (GDR) to address these issues by grouping features
into attributes and indexing them with tuple numbers. In extensive experiments
across different query initializations, dataset modalities, and model
architectures, GDR consistently improves convergence and generalizability.
Visualizations show that our method effectively captures attribute-level
information in features. The source code will be available upon acceptance.
