探索可迁移的同质组以用于组合零样本学习
Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning
AI总结：
针对组合零样本学习中条件依赖导致的属性变化问题，提出同质组表示学习方法（HGRL），通过自适应发现和聚合共享属性的类别学习分布式组中心，平衡可迁移性与可判别性，并集成三个核心组件增强视觉与提示表示能力，在三个基准数据集上验证有效。
摘要：
条件依赖性是组合零样本学习中最棘手的问题之一，导致相同状态（对象）在不同对象（状态）下表现出显著的属性变化。为解决此问题，现有方法常采用多对一或一对一的表示范式。然而，这些极端做法在可迁移性与可判别性的跷跷板中造成了不平衡，往往顾此失彼。相比之下，人类擅长以分层聚类的方式进行类比和推理，直觉地将具有相似属性的类别分组以形成连贯概念。受此启发，我们提出了同质组表示学习（HGRL），这是一种新视角，将状态（对象）表示学习构建为多个同质子组表示学习。HGRL旨在通过自适应地发现和聚合具有共享属性的类别，学习保留特定组判别特征的分布式组中心，从而在语义可迁移性与可判别性之间取得平衡。我们的方法集成了三个核心组件，旨在同时增强模型的视觉和提示表示能力。在三个基准数据集上的大量实验验证了我们方法的有效性。
英文摘要：
Conditional dependency present one of the trickiest problems in Compositional Zero-Shot Learning, leading to significant property variations of the same state (object) across different objects (states). To address this problem, existing approaches often adopt either all-to-one or one-to-one representation paradigms. However, these extremes create an imbalance in the seesaw between transferability and discriminability, favoring one at the expense of the other. Comparatively, humans are adept at analogizing and reasoning in a hierarchical clustering manner, intuitively grouping categories with similar properties to form cohesive concepts. Motivated by this, we propose Homogeneous Group Representation Learning (HGRL), a new perspective formulates state (object) representation learning as multiple homogeneous sub-group representation learning. HGRL seeks to achieve a balance between semantic transferability and discriminability by adaptively discovering and aggregating categories with shared properties, learning distributed group centers that retain group-specific discriminative features. Our method integrates three core components designed to simultaneously enhance both the visual and prompt representation capabilities of the model. Extensive experiments on three benchmark datasets validate the effectiveness of our method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
