Object-centric learning (OCL) aims to learn representations of individual objects within visual scenes without manual supervision, facilitating efficient and effective visual reasoning. Traditional OCL methods primarily employ bottom-up approaches that aggregate homogeneous visual features to represent objects. However, in complex visual environments, these methods often fall short due to the heterogeneous nature of visual features within an object. To address this, we propose a novel OCL framework incorporating a top-down pathway. This pathway first bootstraps the semantics of individual objects and then modulates the model to prioritize features relevant to these semantics. By dynamically modulating the model based on its own output, our top-down pathway enhances the representational quality of objects. Our framework achieves state-of-the-art performance across multiple synthetic and real-world object-discovery benchmarks.

翻译：对象中心学习旨在无需人工监督的情况下学习视觉场景中单个对象的表示，从而促进高效有效的视觉推理。传统的对象中心学习方法主要采用自底向上的方法，通过聚合同质视觉特征来表示对象。然而，在复杂的视觉环境中，由于对象内部视觉特征的异质性，这些方法往往表现不佳。为解决此问题，我们提出了一种融合自顶向下通路的新型对象中心学习框架。该通路首先引导出单个对象的语义信息，然后调制模型以优先处理与这些语义相关的特征。通过基于模型自身输出进行动态调制，我们的自顶向下通路提升了对象的表示质量。我们的框架在多个合成与真实世界对象发现基准测试中均达到了最先进的性能。