Few-Shot Learning with Localization in Realistic Settings

Davis Wertheimer; Bharath Hariharan

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arXiv · 1904.08502

Few-Shot Learning with Localization in Realistic Settings

Davis Wertheimer·Bharath Hariharan

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Abstract

Traditional recognition methods typically require large, artificially-balanced training classes, while few-shot learning methods are tested on artificially small ones. In contrast to both extremes, real world recognition problems exhibit heavy-tailed class distributions, with cluttered scenes and a mix of coarse and fine-grained class distinctions. We show that prior methods designed for few-shot learning do not work out of the box in these challenging conditions, based on a new "meta-iNat" benchmark. We introduce three parameter-free improvements: (a) better training procedures based on adapting cross-validation to meta-learning, (b) novel architectures that localize objects using limited bounding box annotations before classification, and (c) simple parameter-free expansions of the feature space based on bilinear pooling. Together, these improvements double the accuracy of state-of-the-art models on meta-iNat while generalizing to prior benchmarks, complex neural architectures, and settings with substantial domain shift.

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Davis Wertheimer, Bharath Hariharan. 2019-04-09. Few-Shot Learning with Localization in Realistic Settings. https://arxiv.org/abs/1904.08502

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