论文标题
通过多标签学习识别食品成分
Food Ingredients Recognition through Multi-label Learning
论文作者
论文摘要
在通用食品板上识别各种食品项目的能力是自动饮食评估系统的关键决定因素。这项研究激发了对自动饮食评估的需求，并提出了一个实现这一目标的框架。在此框架内，我们专注于视觉识别各种成分的核心功能之一。为此，我们采用了深层的多标签学习方法，并评估了几个最先进的神经网络，以便它们能够检测菜肴图像中任意数量的成分的能力。在这项工作中评估的模型遵循确定的元结构，该结构由编码器和解码器组件组成。评估和基准测试了两个不同的解码方案，一种基于全球平均池基于全球平均池，另一个基于注意机制。对于编码，采用了几种著名的架构，包括densenet，EfficityNet，Mobilenet，Inception和Xception。我们使用具有挑战性的数据集，Nutrition5K提出了有希望的基于深度学习的成分检测的有希望的初步结果，并为将来的探索建立了强大的基线。
The ability to recognize various food-items in a generic food plate is a key determinant for an automated diet assessment system. This study motivates the need for automated diet assessment and proposes a framework to achieve this. Within this framework, we focus on one of the core functionalities to visually recognize various ingredients. To this end, we employed a deep multi-label learning approach and evaluated several state-of-the-art neural networks for their ability to detect an arbitrary number of ingredients in a dish image. The models evaluated in this work follow a definite meta-structure, consisting of an encoder and a decoder component. Two distinct decoding schemes, one based on global average pooling and the other on attention mechanism, are evaluated and benchmarked. Whereas for encoding, several well-known architectures, including DenseNet, EfficientNet, MobileNet, Inception and Xception, were employed. We present promising preliminary results for deep learning-based ingredients detection, using a challenging dataset, Nutrition5K, and establish a strong baseline for future explorations.
