通过想象合成特征，提升零样本组合识别的泛化能力。
Learning by Imagining: Debiased Feature Augmentation for Compositional Zero-Shot Learning
- 用解耦重构框架生成高保真组合特征
- 在三个数据集上达到最先进性能
- 适合研究零样本学习与特征解耦的学者
组合零样本学习（CZSL）旨在通过已知属性和对象的先验知识，识别未见的属性-对象组合。由于属性与对象之间存在纠缠关系，且真实数据中长尾分布普遍，学习可泛化的组合表示仍具挑战。受神经科学发现的启发——想象与感知共享相似神经机制，本文提出去偏特征增强方法（DeFA）。该方法结合解耦-重建框架与去偏策略，通过合成高质量组合特征，显式利用已知属性和对象的先验知识，促进组合泛化。在三个常用数据集上的大量实验表明，DeFA在闭世界和开世界设置下均达到最优性能。
原文摘要 · Abstract (English)
Compositional Zero-Shot Learning (CZSL) aims to recognize unseen attribute-object compositions by learning prior knowledge of seen primitives, \textit{i.e.}, attributes and objects. Learning generalizable compositional representations in CZSL remains challenging due to the entangled nature of attributes and objects as well as the prevalence of long-tailed distributions in real-world data. Inspired by neuroscientific findings that imagination and perception share similar neural processes, we propose a novel approach called Debiased Feature Augmentation (DeFA) to address these challenges. The proposed DeFA integrates a disentangle-and-reconstruct framework for feature augmentation with a debiasing strategy. DeFA explicitly leverages the prior knowledge of seen attributes and objects by synthesizing high-fidelity composition features to support compositional generalization. Extensive experiments on three widely used datasets demonstrate that DeFA achieves state-of-the-art performance in both \textit{closed-world} and \textit{open-world} settings.
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