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Very deep recurrent convolutional neural network for object recognition

机译:非常深的反复卷积神经网络,用于对象识别

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In recent years, Computer vision has become a very active field. This field includes methods for processing, analyzing, and understanding images. The most challenging problems in computer vision are image classification and object recognition. This paper presents a new approach for object recognition task. This approach exploits the success of the Very Deep Convolutional Neural Network for object recognition. In fact, it improves the convolutional layers by adding recurrent connections. This proposed approach was evaluated on two object recognition benchmarks: Pascal VOC 2007 and CIFAR-10. The experimental results prove the efficiency of our method in comparison with the state of the art methods.
机译:近年来,计算机愿景已成为一个非常活跃的领域。该字段包括处理,分析和解图像的方法。计算机视觉中最具挑战性的问题是图像分类和对象识别。本文介绍了对象识别任务的新方法。这种方法利用非常深卷积神经网络的成功进行对象识别。实际上,它通过添加反复连接来改善卷积层。在两个对象识别基准中评估了这种方法:Pascal VOC 2007和CIFAR-10。实验结果证明了我们与现有技术的方法的效率。

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