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|Title:||Structure-aware generation network for recipe generation from images||Authors:||Wang, Hao
Hoi, Steven C. H.
|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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Updated on Jan 23, 2022
Updated on Jan 23, 2022
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