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Paper

Zero-Shot Learning with Structured Embeddings

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons127761

Akata,  Zeynep
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45383

Schiele,  Bernt
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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Fulltext (public)

arXiv:1409.8403.pdf
(Preprint), 281KB

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Citation

Akata, Z., Lee, H., & Schiele, B. (2014). Zero-Shot Learning with Structured Embeddings. Retrieved from http://arxiv.org/abs/1409.8403.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0024-4878-A
Abstract
Despite significant recent advances in image classification, fine-grained classification remains a challenge. In the present paper, we address the zero-shot and few-shot learning scenarios as obtaining labeled data is especially difficult for fine-grained classification tasks. First, we embed state-of-the-art image descriptors in a label embedding space using side information such as attributes. We argue that learning a joint embedding space, that maximizes the compatibility between the input and output embeddings, is highly effective for zero/few-shot learning. We show empirically that such embeddings significantly outperforms the current state-of-the-art methods on two challenging datasets (Caltech-UCSD Birds and Animals with Attributes). Second, to reduce the amount of costly manual attribute annotations, we use alternate output embeddings based on the word-vector representations, obtained from large text-corpora without any supervision. We report that such unsupervised embeddings achieve encouraging results, and lead to further improvements when combined with the supervised ones.