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2018 | OriginalPaper | Buchkapitel

Statistically-Motivated Second-Order Pooling

verfasst von : Kaicheng Yu, Mathieu Salzmann

Erschienen in: Computer Vision – ECCV 2018

Verlag: Springer International Publishing

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Abstract

Second-order pooling, a.k.a. bilinear pooling, has proven effective for deep learning based visual recognition. However, the resulting second-order networks yield a final representation that is orders of magnitude larger than that of standard, first-order ones, making them memory-intensive and cumbersome to deploy. Here, we introduce a general, parametric compression strategy that can produce more compact representations than existing compression techniques, yet outperform both compressed and uncompressed second-order models. Our approach is motivated by a statistical analysis of the network’s activations, relying on operations that lead to a Gaussian-distributed final representation, as inherently used by first-order deep networks. As evidenced by our experiments, this lets us outperform the state-of-the-art first-order and second-order models on several benchmark recognition datasets.

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Titel: Statistically-Motivated Second-Order Pooling
verfasst von: Kaicheng Yu
Mathieu Salzmann
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2018
Print ISBN: 978-3-030-01233-5

Electronic ISBN: 978-3-030-01234-2

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-01234-2_37

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