Food recognition plays an important role in food choice and intake, which is essential to the health and well-being of humans. It is thus of importance to the computer vision community, and can further support many food-oriented vision and multimodal tasks. Unfortunately, we have witnessed remarkable advancements in generic visual recognition for released large-scale datasets, yet largely lags in the food domain. In this paper, we introduce Food2K, which is the largest food recognition dataset with 2,000 categories and over 1 million images.Compared with existing food recognition datasets, Food2K bypasses them in both categories and images by one order of magnitude, and thus establishes a new challenging benchmark to develop advanced models for food visual representation learning. Furthermore, we propose a deep progressive region enhancement network for food recognition, which mainly consists of two components, namely progressive local feature learning and region feature enhancement. The former adopts improved progressive training to learn diverse and complementary local features, while the latter utilizes self-attention to incorporate richer context with multiple scales into local features for further local feature enhancement. Extensive experiments on Food2K demonstrate the effectiveness of our proposed method. More importantly, we have verified better generalization ability of Food2K in various tasks, including food recognition, food image retrieval, cross-modal recipe retrieval, food detection and segmentation. Food2K can be further explored to benefit more food-relevant tasks including emerging and more complex ones (e.g., nutritional understanding of food), and the trained models on Food2K can be expected as backbones to improve the performance of more food-relevant tasks. We also hope Food2K can serve as a large scale fine-grained visual recognition benchmark.

翻译：食物识别在食物选择与摄入中扮演重要角色，这对人类的健康与福祉至关重要。因此，它对计算机视觉领域具有重要意义，并能进一步支持许多以食物为导向的视觉与多模态任务。遗憾的是，尽管我们在已发布的大规模数据集的通用视觉识别方面取得了显著进展，但在食物领域却仍明显滞后。本文引入了Food2K，这是目前最大的食物识别数据集，包含2000个类别和超过100万张图像。与现有食物识别数据集相比，Food2K在类别和图像数量上均高出至少一个数量级，从而为开发食物视觉表示学习的先进模型建立了一个新的挑战性基准。此外，我们提出了一种用于食物识别的深度渐进区域增强网络，该网络主要由两个组件组成：渐进式局部特征学习和区域特征增强。前者采用改进的渐进式训练来学习多样化和互补的局部特征，而后者则利用自注意力将具有多个尺度的更丰富语境融入局部特征，以进一步增强局部特征。在Food2K上的大量实验证明了我们提出方法的有效性。更重要的是，我们在各种任务中验证了Food2K更好的泛化能力，包括食物识别、食物图像检索、跨模态食谱检索、食物检测与分割。Food2K可进一步探索以惠及更多与食物相关的任务，包括新兴或更复杂的任务（例如，食物的营养理解），并且基于Food2K训练的模型有望作为骨干网络提升更多食物相关任务的表现。我们也希望Food2K能作为一个大规模细粒度视觉识别基准。