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• W2746797088 abstract "We address zero-shot learning using a new manifold alignment framework based on a localized multi-scale transform on graphs. Our inference approach includes a smoothness criterion for a function mapping nodes on a graph (visual representation) onto a linear space (semantic representation), which we optimize using multi-scale graph wavelets. The robustness of the ensuing scheme allows us to operate with automatically generated semantic annotations, resulting in an algorithm that is entirely free of manual supervision, and yet improves the state-of-the-art as measured on benchmark datasets." @default.
• W2746797088 created "2017-08-31" @default.
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• W2746797088 date "2017-07-01" @default.
• W2746797088 modified "2023-09-26" @default.
• W2746797088 title "Zero Shot Learning via Multi-scale Manifold Regularization" @default.
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• W2746797088 doi "https://doi.org/10.1109/cvpr.2017.562" @default.
• W2746797088 hasPublicationYear "2017" @default.
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