EVA: Mixture-of-Experts Semantic Variant Alignment for Compositional Zero-Shot Learning

• URL: http://arxiv.org/abs/2506.20986v1
• Date: Thu, 26 Jun 2025 04:00:55 GMT
• Title: EVA: Mixture-of-Experts Semantic Variant Alignment for Compositional Zero-Shot Learning
• Authors: Xiao Zhang, Yongqiang Ma, Haodong Jing, Nanning Zheng,
• Abstract summary: We propose EVA, a Mixture-of-Experts Semantic Variant Alignment framework for Compositional Zero-Shot Learning (CZSL)<n>Specifically, we introduce domain-expert adaption, leveraging multiple experts to achieve token-aware learning and model high-quality primitive representations.<n>Our method significantly outperforms other state-of-the-art CZSL methods on three popular benchmarks in both closed- and open-world settings.
• Score: 31.95599022275838
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Compositional Zero-Shot Learning (CZSL) investigates compositional generalization capacity to recognize unknown state-object pairs based on learned primitive concepts. Existing CZSL methods typically derive primitives features through a simple composition-prototype mapping, which is suboptimal for a set of individuals that can be divided into distinct semantic subsets. Moreover, the all-to-one cross-modal primitives matching neglects compositional divergence within identical states or objects, limiting fine-grained image-composition alignment. In this study, we propose EVA, a Mixture-of-Experts Semantic Variant Alignment framework for CZSL. Specifically, we introduce domain-expert adaption, leveraging multiple experts to achieve token-aware learning and model high-quality primitive representations. To enable accurate compositional generalization, we further present semantic variant alignment to select semantically relevant representation for image-primitives matching. Our method significantly outperforms other state-of-the-art CZSL methods on three popular benchmarks in both closed- and open-world settings, demonstrating the efficacy of the proposed insight.

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