Learning Structural Representations for Recipe Generation and Food Retrieval

• URL: http://arxiv.org/abs/2110.01209v1
• Date: Mon, 4 Oct 2021 06:36:31 GMT
• Title: Learning Structural Representations for Recipe Generation and Food Retrieval
• Authors: Hao Wang, Guosheng Lin, Steven C. H. Hoi, Chunyan Miao
• Abstract summary: We propose a novel framework of Structure-aware Generation Network (SGN) to tackle the food recipe generation task. Our proposed model can produce high-quality and coherent recipes, and achieve the state-of-the-art performance on the benchmark Recipe1M dataset.
• Score: 101.97397967958722
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Food is significant to human daily life. In this paper, we are interested in learning structural representations for lengthy recipes, that can benefit the recipe generation and food retrieval tasks. We mainly investigate an open research task of generating cooking instructions based on food images and ingredients, which is similar to the image captioning task. However, compared with image captioning datasets, the target recipes are lengthy paragraphs and do not have annotations on structure information. To address the above limitations, we propose a novel framework of Structure-aware Generation Network (SGN) to tackle the food recipe generation task. Our approach brings together several novel ideas in a systematic framework: (1) exploiting an unsupervised learning approach to obtain the sentence-level tree structure labels before training; (2) generating trees of target recipes from images with the supervision of tree structure labels learned from (1); and (3) integrating the inferred tree structures into the recipe generation procedure. Our proposed model can produce high-quality and coherent recipes, and achieve the state-of-the-art performance on the benchmark Recipe1M dataset. We also validate the usefulness of our learned tree structures in the food cross-modal retrieval task, where the proposed model with tree representations can outperform state-of-the-art benchmark results.

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