Abstract
Zero-Shot Learning (ZSL) has gained its popularity recently owing to its promising characteristic that requires no training data to recognize new visual classes. One key technique is to transfer knowledge from the seen classes to the new unseen classes in an intermediate embedding space for both visual and textual modalities. Therefore, the construction of the embedding space is extremely important. Manifold embedding is able to well capture the intrinsic structure of the embedding space. To this end, with the assumption that the distribution of the semantic categories in the word vector space has an intrinsic manifold structure, this paper proposes a Manifold Embedding based ZSL (ME-ZSL) approach by formulating the manifold structure for the visual to textual embedding with the intra-class compactness, the inter-class separability, and the locality preservation. The linear, closed-form solution makes ME-ZSL efficient to compute. Extensive experiments on the popular AwA and CUB datasets validate the effectiveness of ME-ZSL.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China under Grants 61771329 and 61632018.
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Yu, Yl., Ji, Z., Pang, Yw. (2018). Zero-Shot Leaning with Manifold Embedding. In: Peng, Y., Yu, K., Lu, J., Jiang, X. (eds) Intelligence Science and Big Data Engineering. IScIDE 2018. Lecture Notes in Computer Science(), vol 11266. Springer, Cham. https://doi.org/10.1007/978-3-030-02698-1_12
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