  Fine-grained domain generalization (FGDG) is a more challenging task than
traditional DG tasks due to its small inter-class variations and relatively
large intra-class disparities. When domain distribution changes, the
vulnerability of subtle features leads to a severe deterioration in model
performance. Nevertheless, humans inherently demonstrate the capacity for
generalizing to out-of-distribution data, leveraging structured
multi-granularity knowledge that emerges from discerning the commonality and
specificity within categories. Likewise, we propose a Feature Structuralized
Domain Generalization (FSDG) model, wherein features experience
structuralization into common, specific, and confounding segments, harmoniously
aligned with their relevant semantic concepts, to elevate performance in FGDG.
Specifically, feature structuralization (FS) is accomplished through joint
optimization of five constraints: a decorrelation function applied to
disentangled segments, three constraints ensuring common feature consistency
and specific feature distinctiveness, and a prediction calibration term. By
imposing these stipulations, FSDG is prompted to disentangle and align features
based on multi-granularity knowledge, facilitating robust subtle distinctions
among categories. Extensive experimentation on three benchmarks consistently
validates the superiority of FSDG over state-of-the-art counterparts, with an
average improvement of 6.2% in FGDG performance. Beyond that, the
explainability analysis on explicit concept matching intensity between the
shared concepts among categories and the model channels, along with experiments
on various mainstream model architectures, substantiates the validity of FS.
