Fine-Grained Zero-Shot Learning with Attribute-Centric Representations

• URL: http://arxiv.org/abs/2512.12219v1
• Date: Sat, 13 Dec 2025 07:12:09 GMT
• Title: Fine-Grained Zero-Shot Learning with Attribute-Centric Representations
• Abstract summary: We propose a zero-shot learning framework that learns AttributeCentric Representations (ACR) to tackle the problem of attribute entanglement.<n>ACR is achieved with two mixture-of-experts components, including Mixture of Patch Experts (MoPE) and Mixture of Attribute Experts (MoAE)<n>Our ACR achieves consistent state-of-the-art results on benchmark datasets CUB, AwA2, and SUN.
• Score: 24.673597914215886
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recognizing unseen fine-grained categories demands a model that can distinguish subtle visual differences. This is typically achieved by transferring visual-attribute relationships from seen classes to unseen classes. The core challenge is attribute entanglement, where conventional models collapse distinct attributes like color, shape, and texture into a single visual embedding. This causes interference that masks these critical distinctions. The post-hoc solutions of previous work are insufficient, as they operate on representations that are already mixed. We propose a zero-shot learning framework that learns AttributeCentric Representations (ACR) to tackle this problem by imposing attribute disentanglement during representation learning. ACR is achieved with two mixture-of-experts components, including Mixture of Patch Experts (MoPE) and Mixture of Attribute Experts (MoAE). First, MoPE is inserted into the transformer using a dual-level routing mechanism to conditionally dispatch image patches to specialized experts. This ensures coherent attribute families are processed by dedicated experts. Finally, the MoAE head projects these expert-refined features into sparse, partaware attribute maps for robust zero-shot classification. On zero-shot learning benchmark datasets CUB, AwA2, and SUN, our ACR achieves consistent state-of-the-art results.

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