  Panoptic Scene Graph Generation (PSG) aims to generate a comprehensive
graph-structure representation based on panoptic segmentation masks. Despite
remarkable progress in PSG, almost all existing methods neglect the importance
of shape-aware features, which inherently focus on the contours and boundaries
of objects. To bridge this gap, we propose a model-agnostic Curricular
shApe-aware FEature (CAFE) learning strategy for PSG. Specifically, we
incorporate shape-aware features (i.e., mask features and boundary features)
into PSG, moving beyond reliance solely on bbox features. Furthermore, drawing
inspiration from human cognition, we propose to integrate shape-aware features
in an easy-to-hard manner. To achieve this, we categorize the predicates into
three groups based on cognition learning difficulty and correspondingly divide
the training process into three stages. Each stage utilizes a specialized
relation classifier to distinguish specific groups of predicates. As the
learning difficulty of predicates increases, these classifiers are equipped
with features of ascending complexity. We also incorporate knowledge
distillation to retain knowledge acquired in earlier stages. Due to its
model-agnostic nature, CAFE can be seamlessly incorporated into any PSG model.
Extensive experiments and ablations on two PSG tasks under both robust and
zero-shot PSG have attested to the superiority and robustness of our proposed
CAFE, which outperforms existing state-of-the-art methods by a large margin.
