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引用次数: 45

Abstract

Due to the recent progress of the studies on deep learning, deep convolutional neural network (DCNN) based methods have outperformed conventional methods with a large margin. Therefore, DCNN-based recognition should be introduced into mobile object recognition. However, since DCNN computation is usually performed on GPU-equipped PCs, it is not easy for mobile devices where memory and computational power is limited.
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DeepFoodCam:基于dcnn的实时移动食品识别系统
由于近年来深度学习研究的进展,基于深度卷积神经网络(deep convolutional neural network, DCNN)的方法已经大大优于传统方法。因此,应该将基于dcnn的识别引入到移动目标识别中。然而,由于DCNN计算通常在配备gpu的pc上执行,因此对于内存和计算能力有限的移动设备来说并不容易。
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Food Image Recognition Using Very Deep Convolutional Networks Session details: Keynote Address Innovative Technology and Dietary Assessment in Low-Income Countries GoCARB: A Smartphone Application for Automatic Assessment of Carbohydrate Intake Session details: Oral Paper Session 1
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