# Geometric Neural Phrase Pooling: Modeling the Spatial Co-occurrence of Neurons Source: [Geometric Neural Phrase Pooling: Modeling the Spatial Co-occurrence of Neurons]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:23:26.300Z Authors: Lingxi Xie, Qi Tian, John Flynn, Jingdong Wang, Alan Yuille Published: 2016-07-21T21:51:58.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()