🤖 AI Summary
This work addresses unsupervised object-centric representation learning by explicitly disentangling shape and texture factors of objects in images, thereby enhancing model robustness to structural variations and cross-object generalization. Methodologically, it is the first to impose a predefined dimensional partitioning of the latent space within an object-centric framework, enforcing strict separation between shape and texture subspaces. Building upon Invariant Slot Attention, the approach introduces prior-driven structural constraints and a dual-branch feature disentanglement architecture, enabling controllable texture generation and cross-shape–texture transfer. Evaluated on multiple standard benchmarks, the method achieves significant improvements in disentanglement quality—measured via established metrics—and consistently outperforms existing baselines across diverse downstream tasks, including segmentation, reconstruction, and compositional generalization. These results empirically validate the effectiveness and practicality of explicit latent-space structural design for object-centric learning.
📝 Abstract
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.