SPGGNN: Semantic-Path Guided Graph Neural Network for Heterogeneous Graph Embedding

Abstract

In this paper, we introduce Recipe1M+, a new large-scale, structured corpus of over one million cooking recipes and 13million 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 yieldsimpressive results on an image-recipe retrieval task. Moreover, we demonstrate that regularization via the addition of a high-levelclassification objective both improves retrieval performance to rival that of humans and enables semantic vector arithmetic. We postulatethat these embeddings will provide a basis for further exploration of the Recipe1M+ dataset and food and cooking in general. Code, dataand models are publicly available.

Check out our most recent journal paper for full details and more analysis! Our CVPR paper can be downloaded from here.

Recipe1M+ dataset

We present the large-scale Recipe1M+ dataset which contains one million structured cooking recipes with 13M associated images.

Follow this link to download the dataset.

Below are the dataset statistics:

Joint embedding

We train a joint embedding composed of an encoder for each modality (ingredients, instructions and images).

Results

im2recipe retrieval

We evaluate all the recipe representations for im2recipe retrieval. Given a food image, the task is to retrieve its recipe from a collection of test recipes.

Comparison with humans

In order to better assess the quality of our embeddings we also evaluate the performance of humans on the im2recipe task.

Examples

Embedding Analysis

We explore whether any semantic concepts emerge in the neuron activations and whether the embedding space has certain arithmetic properties.

Visualizing embedding units

We show the localized unit activations in both image and recipe embeddings. We find that certain units show localized semantic alignment between the embeddings of the two modalities.

Arithmetics

We demonstrate the capabilities of our learned embeddings with simple arithmetic operations. In the context of food recipes, one would expect that:

where v represents the map into the embedding space.

We investigate whether our learned embeddings have such properties by applying the previous equation template to the averaged vectors of recipes that contain the queried words in their title. The figures below show some results with same and cross-modality embedding arithmetics.

Image Embeddings	Recipe Embeddings

Fractional arithmetics

Another type of arithmetic we examine is fractional arithmetic, in which our model interpolates across the vector representations of two concepts in the embedding space. Specifically, we examine the results for:

where varies from 0 to 1.

Image Embeddings	Recipe Embeddings

Embedding visualization

In order to visualize the learned embedding we make use of t-SNE and the 50k recipes with nutritional information we have within Recipe1M+.

Semantic categories

In the figure bellow we show those recipes that belong to the top 12 semantic categories used in our semantic regularization.

Healthiness within the embedding

In the next figure we can see the previous embedding visualization but this time showing the same recipes on different colors depending on how healthy they are in terms of sugar, fat, saturates and salt.

Code & Supplemental material

• Code on GitHub (Tensorflow)
• Supplemental material

Citation

@article{marin2019learning,
  title = {Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images},
  author = {Marin, Javier and Biswas, Aritro and Ofli, Ferda and Hynes, Nicholas and 
  Salvador, Amaia and Aytar, Yusuf and Weber, Ingmar and Torralba, Antonio},
  journal = {{IEEE} Trans. Pattern Anal. Mach. Intell.},
  year = {2019}
}

Acknowledgements

This work has been supported by CSAIL-QCRI collaboration projects and the framework of projects TEC2013-43935-R and TEC2016-75976-R, financed by the Spanish Ministerio de Economia y Competitividad and the European Regional Development Fund (ERDF).