Learning Clique-based Inter-class Affinity for Compositional Zero-shot Learning

Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions of objects and states by transferring knowledge from seen compositions. A critical omission in prior studies is the uniform penalty imposed on all incorrect compositions, ignoring their inherent affinities with the ground-truth labels. This oversight leads to severe overfitting on seen classes and impedes the discovery of genuine visual-semantic relationships. To address this, we propose Clique-based Interclass Affinity (CIA), a framework that introduces hierarchical semantic supervision by grouping compositions into affinity cliques. CIA encodes both semantic affinity and visual affinity to construct multi-level cliques. These cliques guide a one-to-many alignment between visual and semantic features, enabling the model to learn generalizable class prototypes through structured constraints, rather than treating all incorrect classes equally. Unlike prior works focusing on direct classification, CIA emphasizes unveiling intrinsic compositional structures by analyzing inter-semantic and visual relationships. Extensive experiments on MIT-States, UT-Zappos, and C-GQA demonstrate CIA’s superiority, showcasing its robustness in both closed-world and open-world settings. Our code is available at https://github.com/LanchJL/CIA-CZSL.