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arXiv · 1607.06514

Geometric Neural Phrase Pooling: Modeling the Spatial Co-occurrence of Neurons

Lingxi Xie·Qi Tian·John Flynn·Jingdong Wang·Alan Yuille
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Abstract

Deep Convolutional Neural Networks (CNNs) are playing important roles in state-of-the-art visual recognition. This paper focuses on modeling the spatial co-occurrence of neuron responses, which is less studied in the previous work. For this, we consider the neurons in the hidden layer as neural words, and construct a set of geometric neural phrases on top of them. The idea that grouping neural words into neural phrases is borrowed from the Bag-of-Visual-Words (BoVW) model. Next, the Geometric Neural Phrase Pooling (GNPP) algorithm is proposed to efficiently encode these neural phrases. GNPP acts as a new type of hidden layer, which punishes the isolated neuron responses after convolution, and can be inserted into a CNN model with little extra computational overhead. Experimental results show that GNPP produces significant and consistent accuracy gain in image classification.

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Lingxi Xie, Qi Tian, John Flynn, Jingdong Wang, Alan Yuille. 2016-07-21. Geometric Neural Phrase Pooling: Modeling the Spatial Co-occurrence of Neurons. https://arxiv.org/abs/1607.06514

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