Abstract
Humans can discern scene-independent features of objects across various environments, allowing them to swiftly identify objects amidst changing factors such as lighting, perspective, size, and position and imagine the complete images of the same object in diverse settings. Existing object-centric learning methods only extract scene-dependent object-centric representations, lacking the ability to identify the same object across scenes as humans. Moreover, some existing methods discard the individual object generation capabilities to handle complex scenes. This paper introduces a novel object-centric learning method to empower AI systems with human-like capabilities to identify objects across scenes and generate diverse scenes containing specific objects by learning a set of global object-centric representations. To learn the global object-centric representations that encapsulate globally invariant attributes of objects (i.e., the complete appearance and shape), this paper designs a Disentangled Slot Attention module to convert the scene features into scene-dependent attributes (such as scale, position and orientation) and scene-independent representations (i.e., appearance and shape). Experimental results substantiate the efficacy of the proposed method, demonstrating remarkable proficiency in global object-centric representation learning, object identification, scene generation with specific objects and scene decomposition.
Abstract (translated)
人类能够在各种环境中识别出物体的环境无关特征，使他们能够迅速在光照、视角、大小和位置等变化因素中辨别出物体，并想象同一物体在不同场景中的完整图像。现有的以对象为中心的学习方法仅提取了依赖于特定场景的对象表示，缺乏像人类一样跨场景识别同一对象的能力。此外，某些现有方法放弃了生成单个对象的能力来处理复杂的场景。本文介绍了一种新的以对象为中心的学习方法，旨在赋予AI系统类似人类的跨场景识别物体和通过学习一组全局对象中心表示来生成包含特定对象的各种场景的能力。为了学习封装了对象全球不变属性（即完整外观和形状）的全局对象中心表示，本文设计了一个解缠插槽注意力模块将场景特征转换为依赖于场景的属性（如尺度、位置和方向）以及独立于场景的表示（即外观和形状）。实验结果证实了所提出方法的有效性，在全球对象中心表示学习、对象识别、特定对象的场景生成及场景分解方面表现出色。
URL
https://arxiv.org/abs/2410.18809
