Puru Vaish, Meelad Dashti, Christoph Brune, Pascal Mettes, Tatiana Tommasi, Nicola Strisciuglio · 2026 · arXiv
DOI: 10.48550/arXiv.2610.29811 · open-access PDF
构成零射程学习(CZSL)旨在识别先前观察到的对象和属性的新组合。先前的工作表明,大规模的视觉语言模型编码可分解的语义结构,可以重新编译以允许一般化。地质分解嵌入(GDE)框架通过在嵌入分布的Frechet平均点识别嵌入空间中的方向来正式化这一点,这些模型编码了原始的视觉概念。未见的属性对象组合可以通过这些原始的矢量添加来合成。
Machine translation (Meta M2M100); the English original follows.
Compositional zero-shot learning (CZSL) aims to recognize novel combinations of previously observed objects and attributes. Prior work showed that large-scale vision-language models encode decomposable semantic structures that can be recomposed to enable generalization. The Geodesically Decomposable Embeddings (GDE) framework formalizes this by identifying directions in the tangent space at the Frechet mean of the embedding distribution, which encode primitive visual concepts. Unseen attribute-object compositions can be synthesized through vector addition of these primitives. We revisit the as
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