Extracting object-level representations for downstream reasoning tasks is an
emerging area in AI. Learning object-centric representations in an unsupervised
setting presents multiple challenges, a key one being binding an arbitrary
number of object instances to a specialized object slot. Recent object-centric
representation methods like Slot Attention utilize iterative attention to learn
composable representations with dynamic inference level binding but fail to
achieve specialized slot level binding. To address this, in this paper we
propose Unsupervised Conditional Slot Attention using a novel Probabilistic
Slot Dictionary (PSD). We define PSD with (i) abstract object-level property
vectors as key and (ii) parametric Gaussian distribution as its corresponding
value. We demonstrate the benefits of the learnt specific object-level
conditioning distributions in multiple downstream tasks, namely object
discovery, compositional scene generation, and compositional visual reasoning.
We show that our method provides scene composition capabilities and a
significant boost in a few shot adaptability tasks of compositional visual
reasoning, while performing similarly or better than slot attention in object
discovery tasks

本研究提出了一种基于无监督条件化槽注意力和概率槽字典（PSD）的方法，利用抽象的物体属性向量作为关键字、参数化高斯分布作为相应值，来学习特定的物体级别条件分布，并在多个下游任务中展示了其在物体发现、组合场景生成和组合视觉推理方面的优势。在物体发现任务中，我们的方法表现出相似或更好的性能，并在组合视觉推理的少样本适应性任务中显著提高了场景构成能力。