  Deep superpixel algorithms have made remarkable strides by substituting
hand-crafted features with learnable ones. Nevertheless, we observe that
existing deep superpixel methods, serving as mid-level representation
operations, remain sensitive to the statistical properties (e.g., color
distribution, high-level semantics) embedded within the training dataset.
Consequently, learnable features exhibit constrained discriminative capability,
resulting in unsatisfactory pixel grouping performance, particularly in
untrainable application scenarios. To address this issue, we propose the
Content Disentangle Superpixel (CDS) algorithm to selectively separate the
invariant inter-pixel correlations and statistical properties, i.e., style
noise. Specifically, We first construct auxiliary modalities that are
homologous to the original RGB image but have substantial stylistic variations.
Then, driven by mutual information, we propose the local-grid correlation
alignment across modalities to reduce the distribution discrepancy of
adaptively selected features and learn invariant inter-pixel correlations.
Afterwards, we perform global-style mutual information minimization to enforce
the separation of invariant content and train data styles. The experimental
results on four benchmark datasets demonstrate the superiority of our approach
to existing state-of-the-art methods, regarding boundary adherence,
generalization, and efficiency. Code and pre-trained model are available at
https://github.com/rookiie/CDSpixel.
