Extracting structured representations from raw visual data is an important
and long-standing challenge in machine learning. Recently, techniques for
unsupervised learning of object-centric representations have raised growing
interest. In this context, enhancing the robustness of the latent features can
improve the efficiency and effectiveness of the training of downstream tasks. A
promising step in this direction is to disentangle the factors that cause
variation in the data. Previously, Invariant Slot Attention disentangled
position, scale, and orientation from the remaining features. Extending this
approach, we focus on separating the shape and texture components. In
particular, we propose a novel architecture that biases object-centric models
toward disentangling shape and texture components into two non-overlapping
subsets of the latent space dimensions. These subsets are known a priori, hence
before the training process. Experiments on a range of object-centric
benchmarks reveal that our approach achieves the desired disentanglement while
also numerically improving baseline performance in most cases. In addition, we
show that our method can generate novel textures for a specific object or
transfer textures between objects with distinct shapes.

从原始视觉数据中提取结构化表示是机器学习中一个重要且长期存在的挑战。最近，无监督学习目标为客观中心化表示的技术引起了越来越多的关注。本文提出了一种新颖的架构，通过在潜在空间维度的两个不重叠的子集中偏置客观中心化模型，以将形状和纹理成分分离开。在一系列客观中心化基准测试中的实验证明了我们的方法实现了所需的分离性，并在大多数情况下在数值上提高了基准性能。此外，我们还展示了我们的方法可以为特定对象生成新颖的纹理或在具有不同形状的对象之间传递纹理。