HOPE: A Memory-Based and Composition-Aware Framework for Zero-Shot Learning with Hopfield Network and Soft Mixture of Experts

Do Huu Dat, Po-Yuan Mao, Tien Hoang Nguyen, Wray Buntine, Mohammed Bennamoun; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 1101-1110

Compositional Zero-Shot Learning (CZSL) has emerged as an essential paradigm in machine learning aiming to overcome the constraints of traditional zero-shot learning by incorporating compositional thinking into its methodology. Conventional zero-shot learning has difficulty managing unfamiliar combinations of seen and unseen classes because it depends on pre-defined class embeddings. In contrast Compositional Zero-Shot Learning leverages the inherent hierarchies and structural connections among classes creating new class representations by combining attributes components or other semantic elements. In our paper we propose a novel framework that for the first time combines the Modern Hopfield Network with a Mixture of Experts (HOPE) to classify the compositions of previously unseen objects. Specifically the Modern Hopfield Network creates a memory that stores label prototypes and identifies relevant labels for a given input image. Subsequently the Mixture of Expert models integrates the image with the appropriate prototype to produce the final composition classification. Our approach achieves SOTA performance on several benchmarks including MIT-States and UT-Zappos. We also examine how each component contributes to improved generalization.