Year: 2026
Type: conference-paper
Abstract: The core challenge of Compositional Zero-Shot Learning (CZSL) lies in learning representations of sub-concepts (attributes and objects) from seen compositions and recognizing unseen novel compositions.Most existing CZSL methods primarily focus on prompt optimization on the textual side, while overlooking insufficient visual attribute-object subconcepts disentanglement under a text-centric paradigm.To this end, we propose DMSD, a Dual-Modal Semantic Disentanglement framework that jointly models visual and textual information to achieve effective sub-concept disentanglement.Specifically, DMSD introduces a Contextual Prompt Space, enabling both visual and textual modalities to be modeled under unified contextual semantic representations, thereby enhancing their alignment at the latent semantic level.Moreover, we design Visual Sub-concept Prototypes that explicitly extract and model visual sub-concept features, improving the independence and discriminability of visual sub-concept representations.Furthermore, to achieve fine-grained alignment between visual and textual sub-concepts, we propose a Class-Centroid Bridging Module that guides class centroids toward the textual semantic space, thereby ensuring cross-modal semantic consistency.Extensive experiments on three benchmark datasets (MIT-States, UT-Zappos, and C-GQA) demonstrate that DMSD achieves state-of-the-art performance in both closed-world and open-world settings.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166828290