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SUMMARY:Meal Image Generation from Recipe LOCATION:CoRE A (301) DESCRIPTION;ENCODING=QUOTED-PRINTABLE:
Abstract:
Computational food analysis (CFA) has d rawn substantial attention due to its importance in health and general well being. For instance, being able to extract food information including ingre dients and calories from a meal image could help us monitor our daily nutri ent intake and manage our diet. However, the unstructured nature and divers ity of meal images limit utilities of deep learning based CFA.
We inv estigate the possibility of generating structured meal image from a set of ingredients using Generative Adversarial Networks (GANs). To tackle the dif ficulties mentioned above, we propose a two-phase framework: 1. In order to extract ingredients feature, we train an attention based cross-modal assoc iation model to match ingredient sets and their corresponding images in a j oint latent space. 2. The generative adversarial network uses the ingredien ts feature in the latent space as input and generates the corresponding mea l image. Furthermore, a cycle-consistent constraint is added to further imp rove image quality and control appearance. Extensive experiments show our m odel is able to generate meal image corresponding to the ingredients, which could be used to augment existing dataset for solving other CFA problems.< /p> DTSTAMP:20240328T173439Z DTSTART;TZID=America/New_York:20190613T140000 SEQUENCE:0 TRANSP:OPAQUE END:VEVENT END:VCALENDAR