Intervention-Guided Image-Free Classifier Expansion for Fine-Grained Recognition

Image-free zero-shot classifier expansion aims to extend recognition to unseen classes by establishing an association mapping between class semantics and visual classifier weights, without accessing original images. However, most existing methods rely on an association-based optimization mechanism, which struggles to meet the demands for fine-grained classification of visually and semantically similar classes. Since co-occurring attributes dominate class semantics, the model gravitates towards co-occurring attributes rather than discriminative attributes. This leads to geometrically entangled classifier weights, resulting in ambiguous decision boundaries. To address this problem, this paper proposes a novel approach that reframes weight generation through the lens of causal intervention. The method first employs comparative reasoning to disentangle discriminative and co-occurring attributes. Subsequently, it synthesizes intervened semantics to impose invariance and sensitivity constraints. This mechanism compels the model to suppress co-occurring attributes while enhancing sensitivity to discriminative attributes. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches, yielding clearer decision boundaries in fine-grained settings.