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.

类似于人类将视觉场景视为对象，基于目标的学习（OCL）可以将密集图像或视频抽象成稀疏的对象级特征。我们提出了 “Grouped Discrete Representation”（GDR）方法，通过将特征分组为属性并使用元组编号对其进行索引，解决了将特征视为最小单位而忽视特征组成的属性的问题，并且在不同的查询初始化、数据集形式和模型架构的广泛实验中一致提高了收敛性和泛化能力。可视化结果显示我们的方法有效捕捉了特征中的属性级别信息。