A specialized loss mitigates hubness bias in 3D zero-shot learning and sets new state-of-the-art results on ModelNet40, ModelNet10, McGill, and SHREC2015 for both ZSL and GZSL.
A unified approach for conventional zero-shot, generalized zero-shot, and few-shot learning
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Mitigating the Hubness Problem for Zero-Shot Learning of 3D Objects
A specialized loss mitigates hubness bias in 3D zero-shot learning and sets new state-of-the-art results on ModelNet40, ModelNet10, McGill, and SHREC2015 for both ZSL and GZSL.