Deep-based Ingredient Recognition for Cooking Recipe Retrieval

By Jingjing Chen, Chong-Wah Ngo. 2016

425 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Nutrition and Dietetics, Natural Language Processing, Multimodal Learning

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Citations in the corpus

Elucidating image-to-set prediction: An analysis of models, losses and datasets (2019, methodology): “Instead, they care about evaluating their topk predictions [12, 57, 7, 49, 48], by manually fixing k for all samples, or apply a fixed threshold to label probabilities [61, 5] (allowing for different number of images per sample).”
A Survey on Food Computing (2018, background): “For example, [Chen and Ngo 2016] proposed a multi-task deep learning architecture for simultaneous ingredient and food recognition.”
Predicting Visual Features From Text for Image and Video Caption Retrieval (2017, background): “For image retrieval by caption, recent evidence [12] shows that a one-way mapping from the visual to the textual modality outperforms the state-of-the-art subspace based solutions.”
Recipe1M: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images (2018, background): “In a later work, Chen and Ngo [23] presented a dataset containing 110,241 images annotated with 353 ingredient labels and 65,284 recipes, each with a brief introduction, ingredient list, and preparation instructions.”
Inverse Cooking: Recipe Generation From Food Images (2018, background): “Early attempts exploit single-label classification models coupled with binary logistic loss [3], assuming the independence among labels and dropping potentially relevant information.”
Annotating Objects and Relations in User-Generated Videos (2019, background): “In specific domains, such as the fashion and food recognition, the analytic granularity is even refined into landmarks [15] and ingredients [4], respectively, in order to boost the retrieval performance with more discriminative representation.”
Learning Cross-Modal Embeddings for Cooking Recipes and Food Images (2017, background): “In comparison to the current largest dataset in this domain, Recipe1M includes twice as many recipes as [11] and eight times as many images as [6].”