Publication

Inverse Cooking: Recipe Generation from Food Images

Conference Computer Vision and Pattern Recognition (CVPR)


Abstract

People enjoy food photography because they appreciate food. Behind each meal there is a story described in a complex recipe and, unfortunately, by simply looking at a food image we do not have access to its preparation process. Therefore, in this paper we introduce an inverse cooking system that recreates cooking recipes given food images. 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. We extensively evaluate the whole system on the large-scale Recipe1M dataset and show that (1) we improve performance w.r.t. previous baselines for ingredient prediction; (2) we are able to obtain high quality recipes by leveraging both image and ingredients; (3) our system is able to produce more compelling recipes than retrieval-based approaches according to human judgment. We make code and models publicly available at: https://github.com/ facebookresearch/inversecooking.

Related Publications

All Publications

CVPR - June 19, 2021

Robust Audio-Visual Instance Discrimination

Pedro Morgado, Ishan Misra, Nuno Vasconcelos

CVPR - June 19, 2021

Audio-Visual Instance Discrimination with Cross-Modal Agreement

Pedro Morgado, Nuno Vasconcelos, Ishan Misra

NeurIPS - December 1, 2019

Hate Speech in Pixels: Detection of Offensive Memes towards Automatic Moderation

Benet Oriol Sabat, Cristian Canton Ferrer, Xavier Giro-i-Nieto

arXiv - June 19, 2021

Fast and Accurate Model Scaling

Piotr Dollár, Mannat Singh, Ross Girshick

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy