Multi-Granularity Mutual Refinement Network for Zero-Shot Learning

• URL: http://arxiv.org/abs/2511.08163v1
• Date: Wed, 12 Nov 2025 01:44:01 GMT
• Title: Multi-Granularity Mutual Refinement Network for Zero-Shot Learning
• Authors: Ning Wang, Long Yu, Cong Hua, Guangming Zhu, Lin Mei, Syed Afaq Ali Shah, Mohammed Bennamoun, Liang Zhang,
• Abstract summary: Zero-shot learning (ZSL) aims to recognize unseen classes with zero samples by transferring semantic knowledge from seen classes.<n>We propose a network named Multi-Granularity Mutual Refinement Network (Mg-MRN), which refines discriminative and transferable visual features.
• Score: 30.46071677168364
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Zero-shot learning (ZSL) aims to recognize unseen classes with zero samples by transferring semantic knowledge from seen classes. Current approaches typically correlate global visual features with semantic information (i.e., attributes) or align local visual region features with corresponding attributes to enhance visual-semantic interactions. Although effective, these methods often overlook the intrinsic interactions between local region features, which can further improve the acquisition of transferable and explicit visual features. In this paper, we propose a network named Multi-Granularity Mutual Refinement Network (Mg-MRN), which refine discriminative and transferable visual features by learning decoupled multi-granularity features and cross-granularity feature interactions. Specifically, we design a multi-granularity feature extraction module to learn region-level discriminative features through decoupled region feature mining. Then, a cross-granularity feature fusion module strengthens the inherent interactions between region features of varying granularities. This module enhances the discriminability of representations at each granularity level by integrating region representations from adjacent hierarchies, further improving ZSL recognition performance. Extensive experiments on three popular ZSL benchmark datasets demonstrate the superiority and competitiveness of our proposed Mg-MRN method. Our code is available at https://github.com/NingWang2049/Mg-MRN.

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