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Food retrieval is an important task to perform analysis of food-relatedinformation, where we are interested in retrieving relevant information aboutthe queried food item such as ingredients, cooking instructions, etc. In thispaper, we investigate cross-modal retrieval between food images and cookingrecipes. The goal is to learn an embedding of images and recipes in a commonfeature space, such that the corresponding image-recipe embeddings lie close toone another. Two major challenges in addressing this problem are 1) largeintra-variance and small inter-variance across cross-modal food data; and 2)difficulties in obtaining discriminative recipe representations. To addressthese two problems, we propose Semantic-Consistent and Attention-based Networks(SCAN), which regularize the embeddings of the two modalities through aligningoutput semantic probabilities. Besides, we exploit a self-attention mechanismto improve the embedding of recipes. We evaluate the performance of theproposed method on the large-scale Recipe1M dataset, and show that we canoutperform several state-of-the-art cross-modal retrieval strategies for foodimages and cooking recipes by a significant margin.

Cross-Modal Food Retrieval: Learning a Joint Embedding of Food Images and Recipes with Semantic Consistency and Attention Mechanism