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.

研究了自动饮食评估系统中视觉识别食物的核心功能，使用深度多标签学习方法评估了几种最先进的神经网络，以在图像中检测任意数量的成分，并针对编码使用了多个著名的神经网络模型。在使用挑战性数据集 Nutrition5K 上，取得了有希望的初步结果，并为未来的探索奠定了坚实的基础。