  The appearance of the same object may vary in different scene images due to
perspectives and occlusions between objects. Humans can easily identify the
same object, even if occlusions exist, by completing the occluded parts based
on its canonical image in the memory. Achieving this ability is still a
challenge for machine learning, especially under the unsupervised learning
setting. Inspired by such an ability of humans, this paper proposes a
compositional scene modeling method to infer global representations of
canonical images of objects without any supervision. The representation of each
object is divided into an intrinsic part, which characterizes globally
invariant information (i.e. canonical representation of an object), and an
extrinsic part, which characterizes scene-dependent information (e.g., position
and size). To infer the intrinsic representation of each object, we employ a
patch-matching strategy to align the representation of a potentially occluded
object with the canonical representations of objects, and sample the most
probable canonical representation based on the category of object determined by
amortized variational inference. Extensive experiments are conducted on four
object-centric learning benchmarks, and experimental results demonstrate that
the proposed method not only outperforms state-of-the-arts in terms of
segmentation and reconstruction, but also achieves good global object
identification performance.
