  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 informativeness 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.
