Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images

Abstract: In this paper, we introduce Recipe1M+, a new large-scale, structured corpus of over one million cooking recipes and 13 million food images. As the largest publicly available collection of recipe data, Recipe1M+ affords the ability to train high-capacity modelson aligned, multimodal data. Using these data, we train a neural network to learn a joint embedding of recipes and images that yields impressive results on an image-recipe retrieval task. Moreover, we demonstrate that regularization via the addition of a high-level classification objective both improves retrieval performance to rival that of humans and enables semantic vector arithmetic. We postulate that these embeddings will provide a basis for further exploration of the Recipe1M+ dataset and food and cooking in general. Code, data and models are publicly available.

• The paper introduces a large-scale Recipe1M+ dataset to learn cross-modal embeddings between recipes and images.
• It employs a sophisticated neural network architecture combining leading image models and advanced language models.
• Empirical results demonstrate human-level image-to-recipe retrieval performance, underscoring robust multimodal understanding.

Cross-Modal Embeddings for Cooking Recipes and Food Images

The paper presents a significant piece of research in the domain of computer vision and natural language processing by introducing a large-scale dataset designed for learning cross-modal embeddings between cooking recipes and food images. This dataset, described as the largest of its kind, comprises over one million structured cooking recipes paired with 13 million food images, setting a new benchmark for multimodal datasets in culinary AI.

Dataset and Methodology

The dataset facilitates the training of high-capacity models capable of processing aligned multimodal data, thereby supporting various applications, including recipe retrieval tasks. The ability to map both recipes and images into a common embedding space lies at the core of this study. The authors employ a sophisticated neural network architecture to achieve this, enabling integration across text and visual modalities. The network leverages existing advances in image processing, such as VGG-16 and ResNet-50, combined with advanced LLMs for textual descriptions.

Notably, the paper reports formidable results on the image-to-recipe retrieval task, establishing a performance benchmark that rivals human capabilities. Such achievement is underpinned by the use of a semantic regularization mechanism, which adds a high-level classification objective into the network. This modification significantly enhances retrieval performance and even allows for semantic vector arithmetic, indicating deep semantic understanding within the learned embeddings.

Empirical Evaluation

Through rigorous empirical testing, the paper evaluates the efficacy of the proposed cross-modal embeddings. The image-to-recipe and recipe-to-image retrieval tasks serve as primary test beds for the network's capabilities. Remarkably, the embeddings demonstrate superior retrieval accuracy compared to conventional CCA baselines, with substantial improvements in both median rank and recall rates. The results substantiate the robustness of the proposed approach and highlight the challenges inherent in existing datasets that limit performance due to scale and diversity.

Additionally, a comprehensive analysis juxtaposing human and machine performance on the im2recipe retrieval task is provided. Not only does the model match human-level retrieval performance in less complex scenarios, but it also pushes beyond in broader categories with the aid of semantic regularization. This comparison facilitates an informative discussion regarding the potential of AI systems to augment human culinary creativity.

Implications and Future Directions

The implications of this research are both broad and profound. Practically, the development of automated systems that can understand and generate cooking instructions from images could significantly impact culinary education, health applications, and cultural heritage preservation. Theoretically, the findings open up new avenues in the exploration of multimodal data, particularly in the context of creative domains that combine textual instructions with visual outcomes.

The paper discusses potential extensions of the current research, suggesting that the methodologies introduced could be adapted for other instruction-heavy domains such as industrial processes or educational materials. There is also an indication that the vector arithmetic capabilities of the embeddings could be harnessed for novel applications in recipe modification or cross-modal generation.

In conclusion, this research represents a substantive advancement in the understanding of cross-modal embeddings for recipes and images. It sets a new standard for large-scale recipe datasets and offers promising insights into the capabilities of neural networks in mapping complex multimodal data spaces. With the publicly available dataset, code, and models, this work provides a solid foundation for future exploration and innovation in AI-driven culinary applications.