In domain generalization, the knowledge learnt from one or
multiple source domains is transferred to an unseen target domain. In
this work, we propose a novel domain generalization approach for fine-
grained scene recognition. We first propose a semantic scene descriptor
that jointly captures the subtle differences between fine-grained scenes,
while being robust to varying object configurations across domains. We
model the occurrence patterns of objects in scenes, capturing the infor-
mativeness and discriminability of each object for each scene. We then
transform such occurrences into scene probabilities for each scene image.
Second, we argue that scene images belong to hidden semantic topics that
can be discovered by clustering our semantic descriptors. To evaluate the
proposed method, we propose a new fine-grained scene dataset in cross-
domain settings. Extensive experiments on the proposed dataset and
three benchmark scene datasets show the effectiveness of the proposed
approach for fine-grained scene transfer, where we outperform state-of-
the-art scene recognition and domain generalization methods.