miscellaneous-8

Nutrition

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Learning Cross-Modal Embeddings With Adversarial Networks for Cooking Recipes and Food Images

S. Hoi, Doyen Sahoo, Ee-Peng Lim, Hao Wang, Chenghao Liu•Thu May 02 2019

A novel framework Adversarial Cross-Modal Embedding (ACME) is proposed to resolve the cross-modal retrieval task in food domains and achieves the state-of-the-art performance on the benchmark Recipe1M dataset, validating the efficacy of the proposed technique.

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FoodTracker: A Real-time Food Detection Mobile Application by Deep Convolutional Neural Networks

Jianing Sun, K. Radecka, Z. Zilic•Thu Sep 12 2019

A deep convolutional neural network merging with YOLO is built to achieve simultaneous multi-object recognition and localization with nearly 80% mean average precision and is well-suited for mobile devices with negligible inference time and small memory requirements with a deep learning algorithm.

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An Open-Source Dataset on Dietary Behaviors and DASH Eating Plan Optimization Constraints

Farzin Ahmadi, Fardin Ganjkhanloo, K. Ghobadi•Wed Oct 14 2020

A modified dataset based on dietary behaviors of different groups of people, their demographics, and pre-existing conditions, among other factors is provided to facilitate research in linear optimization and constrained inference models.

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Nutribullets Hybrid: Multi-document Health Summarization

R. Barzilay, Darsh J. Shah, L. Yu, Tao Lei•Wed Apr 07 2021

This work introduces a hybrid generation approach inspired by traditional concept-to-text systems for generating comparative summaries that leads to more faithful, relevant and aggregation-sensitive summarization -- while being equally fluent.

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Scaling Language Models: Methods, Analysis & Insights from Training Gopher

A. Mensch, Katie Millican, Roman Ring, Eliza Rutherford, Jacob Menick, Sebastian Borgeaud, Aida Nematzadeh, O. Vinyals, K. Simonyan, Yujia Li, Igor Babuschkin, Po-Sen Huang, Johannes Welbl, K. Kavukcuoglu, D. Hassabis, James Bradbury, Chris Dyer, L. Sifre, George van den Driessche, Erich Elsen, D. Budden, G. Irving, Laura Weidinger, Maribeth Rauh, Iason Gabriel, William S. Isaac, Saffron Huang, Lisa Anne Hendricks, I. Higgins, Diego de Las Casas, Jack W. Rae, Siddhant M. Jayakumar, Xiang Lorraine Li, Francis Song, Cyprien de Masson d'Autume, Amy Wu, Angeliki Lazaridou, A. Kuncoro, Michela Paganini, Trevor Cai, Jordan Hoffmann, John Aslanides, Sarah Henderson, Susannah Young, Tom Hennigan, Albin Cassirer, Richard Powell, Amelia Glaese, Sumanth Dathathri, Jonathan Uesato, John F. J. Mellor, Antonia Creswell, Nat McAleese, Elena Buchatskaya, Esme Sutherland, Lena Martens, E. Gribovskaya, Domenic Donato, Jean-Baptiste Lespiau, M. Tsimpoukelli, N. Grigorev, Doug Fritz, Thibault Sottiaux, Mantas Pajarskas, Tobias Pohlen, Z. Gong, Daniel Toyama, Tayfun Terzi, Vladimir Mikulik, Aidan Clark, Aurelia Guy, Chris Jones, Matthew G. Johnson, Blake A. Hechtman, Edward Lockhart, Simon Osindero, Laura Rimell, Kareem W. Ayoub, J. Stanway, L. Bennett•Tue Dec 07 2021

This paper presents an analysis of Transformer-based language model performance across a wide range of model scales -- from models with tens of millions of parameters up to a 280 billion parameter model called Gopher.

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MUSEFood: Multi-Sensor-Based Food Volume Estimation on Smartphones

Liantao Ma, Wen Tang, Yasha Wang, Junyi Gao, Weihao Tan•Sun Mar 17 2019

A novel method, named MUSEFood, for food volume estimation, which outperforms state-of-the-art approaches, and highly improves the speed of foodVolume estimation.

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Market2Dish: Health-aware Food Recommendation

Wenjie Wang, Liqiang Nie, Xuemeng Song, Ling-Yu Duan, Hao Jiang, Peiguang Jing•Thu Dec 10 2020

A personalized health-aware food recommendation scheme, namely, Market2Dish, mapping the ingredients displayed in the market to the healthy dishes eaten at home and a novel category-aware hierarchical memory network–based recommender to learn thehealth-aware user-recipe interactions for better food recommendation is presented.

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Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph

Yu Chen, Mohammed J. Zaki, Ananya Subburathinam, Ching-Hua Chen•Mon Jan 04 2021

A KBQA-based personalized food recommendation framework which is equipped with novel techniques for handling negations and numerical comparisons in the queries, and is able to recommend more relevant and healthier recipes.

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Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food

Quin Thames, Arjun Karpur, W. Norris, Fangting Xia, Liviu Panait, Tobias Weyand, Jack Sim•Wed Mar 03 2021

This work introduces Nutrition5k, a novel dataset of 5k diverse, real world food dishes with corresponding video streams, depth images, component weights, and high accuracy nutritional content annotation, and presents a baseline for incorporating depth sensor data to improve nutrition predictions.

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Nutri-bullets: Summarizing Health Studies by Composing Segments

R. Barzilay, Darsh J. Shah, L. Yu, Tao Lei•Sun Mar 21 2021

This work introduces Nutri-bullets, a multi-document summarization task for health and nutrition, and proposes a novel extract-compose model to solve the problem in the regime of limited parallel data.

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