10 papers with code • 5 benchmarks • 3 datasets
Our system predicts ingredients as sets by means of a novel architecture, modeling their dependencies without imposing any order, and then generates cooking instructions by attending to both image and its inferred ingredients simultaneously.
Existing approaches to recipe generation are unable to create recipes for users with culinary preferences but incomplete knowledge of ingredients in specific dishes.
Interests in the automatic generation of cooking recipes have been growing steadily over the past few years thanks to a large amount of online cooking recipes.
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.
The large population of home cooks with dietary restrictions is under-served by existing cooking resources and recipe generation models.
Although pretrained language models can generate fluent recipe texts, they fail to truly learn and use the culinary knowledge in a compositional way.
Our models outperform 10x larger language models without instruction tuning on various tasks such as story/recipe generation and long-form question answering.