ambulance bed bolt briefcase calendar chain chevron-left chevron-right clock-o commenting-o commenting comments diamond envelope-o envelope facebook feed flask globe group heart-o heart heartbeat hospital-o instagram leaf map-marker medkit phone quote-left quote-right skype star-o star tint trophy twitter user-md user youtube

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

Xie L, Tian Q, Flynn J, Wang J, and Yuille A | ECCV 2016 | 2016 | PDF
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.