通过学习状态与物体的原始关系，提升未知组合的零样本识别能力
Learning Primitive Relations for Compositional Zero-Shot Learning
- 引入交叉注意力捕捉状态与物体间的依赖关系
- 在三个基准数据集上均超越现有最优方法
- 适合研究零样本学习与组合推理的学者
组合零样本学习（CZSL）旨在通过已见组合的知识识别未见的状态-物体组合。现有方法通常独立预测状态和物体，忽略了它们之间的关系。本文提出一种新框架——学习原始关系（LPR），通过交叉注意力机制概率性地建模状态与物体之间的关系，使模型能够推断未见组合的可能性。实验结果表明，LPR在所有三个CZSL基准数据集上，无论封闭世界还是开放世界设置下，均优于当前最佳方法。定性分析显示，LPR确实利用状态-物体关系进行未见组合预测。
原文摘要 · Abstract (English)
Compositional Zero-Shot Learning (CZSL) aims to identify unseen state-object compositions by leveraging knowledge learned from seen compositions. Existing approaches often independently predict states and objects, overlooking their relationships. In this paper, we propose a novel framework, learning primitive relations (LPR), designed to probabilistically capture the relationships between states and objects. By employing the cross-attention mechanism, LPR considers the dependencies between states and objects, enabling the model to infer the likelihood of unseen compositions. Experimental results demonstrate that LPR outperforms state-of-the-art methods on all three CZSL benchmark datasets in both closed-world and open-world settings. Through qualitative analysis, we show that LPR leverages state-object relationships for unseen composition prediction.
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