Multi-Label Zero-Shot Learning with Transfer-Aware Label Embedding Projection

Meng Ye; Yuhong Guo

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arXiv · 1808.02474

Multi-Label Zero-Shot Learning with Transfer-Aware Label Embedding Projection

Meng Ye·Yuhong Guo

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Abstract

Zero-shot learning transfers knowledge from seen classes to novel unseen classes to reduce human labor of labelling data for building new classifiers. Much effort on zero-shot learning however has focused on the standard multi-class setting, the more challenging multi-label zero-shot problem has received limited attention. In this paper we propose a transfer-aware embedding projection approach to tackle multi-label zero-shot learning. The approach projects the label embedding vectors into a low-dimensional space to induce better inter-label relationships and explicitly facilitate information transfer from seen labels to unseen labels, while simultaneously learning a max-margin multi-label classifier with the projected label embeddings. Auxiliary information can be conveniently incorporated to guide the label embedding projection to further improve label relation structures for zero-shot knowledge transfer. We conduct experiments for zero-shot multi-label image classification. The results demonstrate the efficacy of the proposed approach.

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BibTeX RIS

Meng Ye, Yuhong Guo. 2018-08-07. Multi-Label Zero-Shot Learning with Transfer-Aware Label Embedding Projection. https://arxiv.org/abs/1808.02474

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