Explicitly Disentangled Representations in Object-Centric Learning
Riccardo Majellaro Jonathan Collu Aske Plaat Thomas M. Moerland
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.