论文标题
采矿判别食品区域，以准确食品识别
Mining Discriminative Food Regions for Accurate Food Recognition
论文作者
论文摘要
自动食品识别是迈向被动饮食监测的第一步。在本文中，我们通过开采歧视性食品地区解决了食品识别问题。从对抗性擦除中汲取灵感，该策略逐渐发现判别性对象区域以进行弱监督的语义细分，我们提出了一种新型的网络体系结构，其中主要网络保持了对输入图像进行分类的基本准确性，一个辅助网络对抗性网络对抗性网络对区分的食物区域进行分类，并分类了区别网络的分类区域，并将结果分类为结果。然后将全局（原始输入图像）和本地（矿区）表示为最终预测。拟议的架构表示为Par-NET，是端到端的训练，并以在线方式突出显示歧视区域。此外，我们推出了一个名为Sushi-50的新的细粒食品数据集，该数据集由50种不同的寿司类别组成。已经进行了广泛的实验来评估所提出的方法。在选择的三个食物数据集（Food-101，Vireo-172和Sushi-50）上，我们的方法始终如一地执行并取得了最新的结果（TOP-1测试准确性$ 90.4 \％$ \％$，$ 90.2 \％$，$ 92.0 \％\％$ $），与其他现有方法相比。数据集和代码可在https://github.com/jianing-qiu/parnet上找到
Automatic food recognition is the very first step towards passive dietary monitoring. In this paper, we address the problem of food recognition by mining discriminative food regions. Taking inspiration from Adversarial Erasing, a strategy that progressively discovers discriminative object regions for weakly supervised semantic segmentation, we propose a novel network architecture in which a primary network maintains the base accuracy of classifying an input image, an auxiliary network adversarially mines discriminative food regions, and a region network classifies the resulting mined regions. The global (the original input image) and the local (the mined regions) representations are then integrated for the final prediction. The proposed architecture denoted as PAR-Net is end-to-end trainable, and highlights discriminative regions in an online fashion. In addition, we introduce a new fine-grained food dataset named as Sushi-50, which consists of 50 different sushi categories. Extensive experiments have been conducted to evaluate the proposed approach. On three food datasets chosen (Food-101, Vireo-172, and Sushi-50), our approach performs consistently and achieves state-of-the-art results (top-1 testing accuracy of $90.4\%$, $90.2\%$, $92.0\%$, respectively) compared with other existing approaches. Dataset and code are available at https://github.com/Jianing-Qiu/PARNet
