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Cross-modal Recipe Retrieval with Rich Food Attributes

Jing-jing Chen, Chong-Wah Ngo, Tat-Seng Chua
2017 Proceedings of the 2017 ACM on Multimedia Conference - MM '17  
With a multi-task deep learning model, this paper provides insights on the feasibility of predicting ingredient, cu ing and cooking a ributes for food recognition and recipe retrieval.  ...  Food is rich of visible (e.g., colour, shape) and procedural (e.g., cu ing, cooking) a ributes.  ...  Deep-based ingredient recognition has also been recently investigated. In [6] , a VGG multi-task learning framework is proposed for simultaneous recognition of food categories and ingredient labels.  ... 
doi:10.1145/3123266.3123428 dblp:conf/mm/ChenNC17 fatcat:5eslho7r3rffleuwxmjs3nkgmy

A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment [article]

Ghalib Tahir, Chu Kiong Loo
2021 arXiv   pre-print
First, we will present the rationale of visual-based methods for food recognition.  ...  This survey discusses the most performing methodologies that have been developed so far for automatic food recognition and volume estimation.  ...  However, such applications hardly offer features for automated food ingredient recognition. For this purpose, several proposed models use multi-label learning for food ingredient recognition.  ... 
arXiv:2106.11776v3 fatcat:kockhutzizdyplcxikcffyid4a

A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment

Ghalib Ahmed Tahir, Chu Kiong Loo
2021 Healthcare  
Similarly, all surveyed studies employed a variant of convolutional neural networks (CNN) for ingredient recognition due to recent research interest.  ...  Our findings indicate that around 66.7% of surveyed studies use visual features from deep neural networks for food recognition.  ...  Conflicts of Interest: The authors wish to confirm that there are no conflicts of interest.  ... 
doi:10.3390/healthcare9121676 pmid:34946400 pmcid:PMC8700885 fatcat:aeq6xfascfhwzjlokulmjlhxve

Recognition and localization of food in cooking videos

Nachwa Aboubakr, Remi Ronfard, James Crowley
2018 Proceedings of the Joint Workshop on Multimedia for Cooking and Eating Activities and Multimedia Assisted Dietary Management - CEA/MADiMa '18  
We describe production of a new data set that provides annotated images for food types and food states.  ...  We compare results with two techniques for detecting food types and food states, and then show that recognizing type and state with separate classifiers improves recognition results.  ...  The number of filters of this layer is equal to the number of values a concept can take. We study this problem as a multi-label classification problem.  ... 
doi:10.1145/3230519.3230590 dblp:conf/ijcai/BakrRC18 fatcat:5ryllmwjf5hldenkzojuttldgm

Vision-Based Food Analysis for Automatic Dietary Assessment [article]

Wei Wang, Weiqing Min, Tianhao Li, Xiaoxiao Dong, Haisheng Li, Shuqiang Jiang
2021 arXiv   pre-print
Key findings and conclusions: After thorough exploration, we find that multi-task end-to-end deep learning approaches are one important trend of VBDA.  ...  The prosperity of deep learning makes VBDA gradually move to an end-to-end implementation, which applies food images to a single network to directly estimate the nutrition.  ...  KZ202110011017) and National Natural Science Foundation of China (grant No. 61877002, 61972378, U1936203, U19B2040).  ... 
arXiv:2108.02947v1 fatcat:c4vonu7im5gwldeyzc2o2g2kye

Mixed-dish Recognition with Contextual Relation Networks

Lixi Deng, Jingjing Chen, Qianru Sun, Xiangnan He, Sheng Tang, Zhaoyan Ming, Yongdong Zhang, Tat Seng Chua
2019 Proceedings of the 27th ACM International Conference on Multimedia - MM '19  
Extensive experiments on both our dataset and a smaller-scale public dataset validate that our CR-Nets can achieve top performance for localizing the dishes and recognizing their food categories.  ...  Mixed dish is a food category that contains different dishes mixed in one plate, and is popular in Eastern and Southeast Asia.  ...  ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (61525206,61572472,61871004) and NExT++ project supported by National Research Foundation, Prime Minister's Office  ... 
doi:10.1145/3343031.3351147 dblp:conf/mm/DengCS0TMZC19 fatcat:m33sdpc5dnau7pjxsaofetkwle

A Delicious Recipe Analysis Framework for Exploring Multi-Modal Recipes with Various Attributes

Weiqing Min, Shuqiang Jiang, Shuhui Wang, Jitao Sang, Shuhuan Mei
2017 Proceedings of the 2017 ACM on Multimedia Conference - MM '17  
Many factors like ingredients, visual appearance, courses (e.g., breakfast and lunch), avor and geographical regions a ect our food perception and choice.  ...  We then utilize a multi-modal embedding method to build the correlation between the learned textual theme features from MATM and visual features from the deep learning network.  ...  Region-Oriented Multi-dimensional Food Summary In this task, we summarize regional foods based on representative ingredients, representative recipe images, avor and course patterns.  ... 
doi:10.1145/3123266.3123272 dblp:conf/mm/MinJWSM17 fatcat:pl2gtzi2dbfexe6zd3wqzaymva

A Framework to Estimate the Nutritional Value of Food in Real Time Using Deep Learning Techniques

Raza Yunus, Omar Arif, Hammad Afzal, Muhammad Faisal Amjad, Haider Abbas, Hira Noor Bokhari, Syeda Tazeen Haider, Nauman Zafar, Raheel Nawaz
2019 IEEE Access  
Our method employs different deep learning models for accurate food identification.  ...  This paper proposes a novel system to automatically estimate food attributes such as ingredients and nutritional value by classifying the input image of food.  ...  Our goal is to minimize the user input and automate this task as much as possible. We employ deep neural networks for estimating the ingredients and the attributes of the food.  ... 
doi:10.1109/access.2018.2879117 fatcat:p6reb6tryzhvzg2lvc5qb54efu

A Large-Scale Benchmark for Food Image Segmentation [article]

Xiongwei Wu, Xin Fu, Ying Liu, Ee-Peng Lim, Steven C.H. Hoi, Qianru Sun
2021 arXiv   pre-print
Food image segmentation is a critical and indispensible task for developing health-related applications such as estimating food calories and nutrients.  ...  Existing food image segmentation models are underperforming due to two reasons: (1) there is a lack of high quality food image datasets with fine-grained ingredient labels and pixel-wise location masks  ...  We hope our dataset can inspire more efforts for the task of food image segmentation. Semantic Segmentation in Images. Deep learning based semantic segmentation is a super hot topic in recent years.  ... 
arXiv:2105.05409v1 fatcat:buqw4czjjrc53cghcvkqwa5c2m

Attend and Guide (AG-Net): A Keypoints-driven Attention-based Deep Network for Image Recognition

Asish Bera, Zachary Wharton, Yonghuai Liu, Nik Bessis, Ardhendu Behera
2021 IEEE Transactions on Image Processing  
This framework applies to traditional and fine-grained image recognition tasks and does not require manually annotated regions (e.g. bounding-box of body parts, objects, etc.) for learning and prediction  ...  The "usefulness" of these SRs for image recognition is measured using our innovative attentional mechanism focusing on parts of the image that are most relevant to a given task.  ...  We thank the Associate Editor and three anonymous reviewers for their constructive comments that have improved the quality of the paper.  ... 
doi:10.1109/tip.2021.3064256 pmid:33705316 fatcat:ii6iozzjw5aptoeofoyq3yqeci

An End-to-End Deep Neural Architecture for Optical Character Verification and Recognition in Retail Food Packaging

Fabio De Sousa Ribeiro, Liyun Gong, Francesco Caliva, Mark Swainson, Kjartan Gudmundsson, Miao Yu, Georgios Leontidis, Xujiong Ye, Stefanos Kollias
2018 2018 25th IEEE International Conference on Image Processing (ICIP)  
In this work, an end-to-end architecture, composed of a dual deep neural network based system is proposed for automatic recognition of use by dates in food package photos.  ...  There exist various types of information in retail food packages, including food product name, ingredients list and use by date.  ...  Limited Company, for the project Automated Robotic Food Manufacturing System.  ... 
doi:10.1109/icip.2018.8451555 dblp:conf/icip/RibeiroGCSG0LYK18 fatcat:ijnvfwq3yrcwxfuzt7lol7rxhm

Picture-to-Amount (PITA): Predicting Relative Ingredient Amounts from Food Images [article]

Jiatong Li, Fangda Han, Ricardo Guerrero, Vladimir Pavlovic
2020 arXiv   pre-print
In this paper, we study the novel and challenging problem of predicting the relative amount of each ingredient from a food image.  ...  More specifically, we predict the ingredient amounts using a domain-driven Wasserstein loss from image-to-recipe cross-modal embeddings learned to align the two views of food data.  ...  [4] used multi-task deep learning and a graph modeling ingredient co-occurrences.  ... 
arXiv:2010.08727v1 fatcat:wymhytygzvcwzfn3jl2zuix33m

Market2Dish: Health-aware Food Recommendation [article]

Wenjie Wang, Ling-yu Duan, Hao Jiang, Peiguang Jing, Xuemeng Song, Liqiang Nie
2020 arXiv   pre-print
For the health-aware food recommendation, we present a novel category-aware hierarchical memory network-based recommender to learn the health-aware user-recipe interactions for better food recommendation  ...  With the rising incidence of some diseases, such as obesity and diabetes, a healthy diet is arousing increasing attention.  ...  Notably, an image usually contains multiple ingredients, and thus the ingredient recognition is essentially a task of multi-label image classification.  ... 
arXiv:2012.06416v1 fatcat:unurz64vvje7nbojj6hyu5xhca

Combining Weakly and Webly Supervised Learning for Classifying Food Images [article]

Parneet Kaur, Karan Sikka, Ajay Divakaran
2017 arXiv   pre-print
Food classification from images is a fine-grained classification problem. Manual curation of food images is cost, time and scalability prohibitive.  ...  To tackle the issue of weak labels, we augment the deep model with Weakly Supervised learning (WSL) that results in an increase in performance to 76.2%.  ...  state-of-theart deep learning methods for food recognition and localization have led to significant improvement in performance [23, 24, 22, 33, 28, 7] .  ... 
arXiv:1712.08730v1 fatcat:idmjxibon5fj5blkls4zz5cupe

Cross-Modal Food Retrieval: Learning a Joint Embedding of Food Images and Recipes with Semantic Consistency and Attention Mechanism [article]

Hao Wang, Doyen Sahoo, Chenghao Liu, Ke Shu, Palakorn Achananuparp, Ee-peng Lim, Steven C. H. Hoi
2021 arXiv   pre-print
Food retrieval is an important task to perform analysis of food-related information, where we are interested in retrieving relevant information about the queried food item such as ingredients, cooking  ...  The goal is to learn an embedding of images and recipes in a common feature space, such that the corresponding image-recipe embeddings lie close to one another.  ...  Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.  ... 
arXiv:2003.03955v3 fatcat:aqy7vykr5favzdfhhamkz3k6wa
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