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Found 9 papers, 1 papers with code
Deep Learning Based Named Entity Recognition Models for Recipes
Simultaneously, we systematically cleaned and analyzed ingredient phrases from RecipeDB, the gold-standard recipe data repository, and annotated them using the Stanford NER.
Dish detection in food platters: A framework for automated diet logging and nutrition management
By comparing with the state-of-the-art model for the IndianFood10 dataset, we demonstrate the superior object detection performance of YOLOv8x for this subset and establish Resnet152 as the best architecture for multi-label classification.
A generative grammar of cooking
The fundamental rules that drive the act of cooking, shaping recipe composition and cuisine architecture, are unclear.
Object Detection in Indian Food Platters using Transfer Learning with YOLOv4
We solve these issues by providing a comprehensively labelled Indian food dataset- IndianFood10, which contains 10 food classes that appear frequently in a staple Indian meal and using transfer learning with YOLOv4 object detector model.
FlavorDB2: An Updated Database of Flavor Molecules
Flavor is expressed through interaction of molecules via gustatory and olfactory mechanisms.
Ratatouille: A tool for Novel Recipe Generation
Due to availability of a large amount of cooking recipes online, there is a growing interest in using this as data to create novel recipes.
Building Hierarchically Disentangled Language Models for Text Generation with Named Entities
Named entities pose a unique challenge to traditional methods of language modeling.
Classification of Cuisines from Sequentially Structured Recipes
These cuisines are characterized in terms of their substructures such as ingredients, cooking processes and utensils.
A Named Entity Based Approach to Model Recipes
Traditional cooking recipes follow a structure which can be modelled very well if the rules and semantics of the different sections of the recipe text are analyzed and represented accurately.
