Learning Program Representations for Food Images and Cooking Recipes

• URL: http://arxiv.org/abs/2203.16071v1
• Date: Wed, 30 Mar 2022 05:52:41 GMT
• Title: Learning Program Representations for Food Images and Cooking Recipes
• Authors: Dim P. Papadopoulos, Enrique Mora, Nadiia Chepurko, Kuan Wei Huang, Ferda Ofli and Antonio Torralba
• Abstract summary: We propose to represent cooking recipes and food images as cooking programs. A model is trained to learn a joint embedding between recipes and food images via self-supervision. We show that projecting the image-recipe embeddings into programs leads to better cross-modal retrieval results.
• Score: 26.054436410924737
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we are interested in modeling a how-to instructional procedure, such as a cooking recipe, with a meaningful and rich high-level representation. Specifically, we propose to represent cooking recipes and food images as cooking programs. Programs provide a structured representation of the task, capturing cooking semantics and sequential relationships of actions in the form of a graph. This allows them to be easily manipulated by users and executed by agents. To this end, we build a model that is trained to learn a joint embedding between recipes and food images via self-supervision and jointly generate a program from this embedding as a sequence. To validate our idea, we crowdsource programs for cooking recipes and show that: (a) projecting the image-recipe embeddings into programs leads to better cross-modal retrieval results; (b) generating programs from images leads to better recognition results compared to predicting raw cooking instructions; and (c) we can generate food images by manipulating programs via optimizing the latent code of a GAN. Code, data, and models are available online.

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