Prototype-Guided Curriculum Learning for Zero-Shot Learning

• URL: http://arxiv.org/abs/2508.07771v1
• Date: Mon, 11 Aug 2025 08:56:21 GMT
• Title: Prototype-Guided Curriculum Learning for Zero-Shot Learning
• Authors: Lei Wang, Shiming Chen, Guo-Sen Xie, Ziming Hong, Chaojian Yu, Qinmu Peng, Xinge You,
• Abstract summary: We propose a prototype-guided curriculum learning framework (dubbed as CLZSL)<n>The PCL module prioritizes samples with high cosine similarity between their visual mappings and the class-level semantic prototypes.<n>The PUP module dynamically updates the class-level semantic prototypes by leveraging the visual mappings learned from instances.
• Score: 25.632658478653855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In Zero-Shot Learning (ZSL), embedding-based methods enable knowledge transfer from seen to unseen classes by learning a visual-semantic mapping from seen-class images to class-level semantic prototypes (e.g., attributes). However, these semantic prototypes are manually defined and may introduce noisy supervision for two main reasons: (i) instance-level mismatch: variations in perspective, occlusion, and annotation bias will cause discrepancies between individual sample and the class-level semantic prototypes; and (ii) class-level imprecision: the manually defined semantic prototypes may not accurately reflect the true semantics of the class. Consequently, the visual-semantic mapping will be misled, reducing the effectiveness of knowledge transfer to unseen classes. In this work, we propose a prototype-guided curriculum learning framework (dubbed as CLZSL), which mitigates instance-level mismatches through a Prototype-Guided Curriculum Learning (PCL) module and addresses class-level imprecision via a Prototype Update (PUP) module. Specifically, the PCL module prioritizes samples with high cosine similarity between their visual mappings and the class-level semantic prototypes, and progressively advances to less-aligned samples, thereby reducing the interference of instance-level mismatches to achieve accurate visual-semantic mapping. Besides, the PUP module dynamically updates the class-level semantic prototypes by leveraging the visual mappings learned from instances, thereby reducing class-level imprecision and further improving the visual-semantic mapping. Experiments were conducted on standard benchmark datasets-AWA2, SUN, and CUB-to verify the effectiveness of our method.

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