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arXiv · 1901.01868

Low-Shot Learning from Imaginary 3D Model

Frederik Pahde·Mihai Puscas·Jannik Wolff·Tassilo Klein·Nicu Sebe·Moin Nabi
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Abstract

Since the advent of deep learning, neural networks have demonstrated remarkable results in many visual recognition tasks, constantly pushing the limits. However, the state-of-the-art approaches are largely unsuitable in scarce data regimes. To address this shortcoming, this paper proposes employing a 3D model, which is derived from training images. Such a model can then be used to hallucinate novel viewpoints and poses for the scarce samples of the few-shot learning scenario. A self-paced learning approach allows for the selection of a diverse set of high-quality images, which facilitates the training of a classifier. The performance of the proposed approach is showcased on the fine-grained CUB-200-2011 dataset in a few-shot setting and significantly improves our baseline accuracy.

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Frederik Pahde, Mihai Puscas, Jannik Wolff, Tassilo Klein, Nicu Sebe, Moin Nabi. 2019-01-04. Low-Shot Learning from Imaginary 3D Model. https://arxiv.org/abs/1901.01868

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