Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150963
Title: Structure-aware generation network for recipe generation from images
Authors: Wang, Hao
Lin, Guosheng
Hoi, Steven C. H.
Miao, Chunyan
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Wang, H., Lin, G., Hoi, S. C. H. & Miao, C. (2020). Structure-aware generation network for recipe generation from images. 2020 European Conference on Computer Vision (ECCV’20), 12372 LNCS, 359-374. https://dx.doi.org/10.1007/978-3-030-58583-9_22
Abstract: Sharing food has become very popular with the development of social media. For many real-world applications, people are keen to know the underlying recipes of a food item. In this paper, we are interested in automatically generating cooking instructions for food. We investigate an open research task of generating cooking instructions based on only food images and ingredients, which is similar to the image captioning task. However, compared with image captioning datasets, the target recipes are long-length 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 with 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.
URI: https://hdl.handle.net/10356/150963
ISBN: 9783030585822
DOI: 10.1007/978-3-030-58583-9_22
Rights: © 2020 Springer Nature Switzerland AG. This is a post-peer-review, pre-copyedit version of an article published in 2020 European Conference on Computer Vision (ECCV’20). The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-58583-9_22
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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