Improved Very Deep Recurrent Convolutional Neural Network for Object Recognition

Abstract

Recently, object recognition has been a very active field of interest. The success of the deep learning based methods in recognizing objects has encouraged recent works to follow this approach. In this paper, we propose a very deep recurrent convolutional neural network approach for object recognition. Our approach uses a very deep convolutional neural network reinforced by integrating recurrent connections to the convolutional layers. Besides, the pooling step has been improved by using two main techniques: the Generalizing Pooling and the spatial pyramid pooling. The Generalizing pooling, that replaces the maxpooling layer commonly used in convolutional neural network, combines pooling operations within a hierarchical tree structure. The Spatial Pyramid Pooling which enables the removal of the fixed size constraint of input image has been conducted. In addition, the data augmentation technique has been used to strengthen the training process. Experiments on three object recognition benchmarks dataset: Pascal VOC 2007, CIFAR-10 and CIFAR-100, have shown the success of our approach.