Oliveira, L.[Luciano],
Costa, V.[Victor],
Neves, G.[Gustavo],
Oliveira, T.[Talmai],
Jorge, E.[Eduardo],
Lizarraga, M.[Miguel],
A mobile, lightweight, poll-based food identification system,
PR(47), No. 5, 2014, pp. 1941-1952.
Elsevier DOI
1412
Food identification
BibRef
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Jiang, S.,
Wang, S.,
Song, X.,
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Geolocalized Modeling for Dish Recognition,
MultMed(17), No. 8, August 2015, pp. 1187-1199.
IEEE DOI
1506
Accuracy. Food dishes. Context.
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Martinel, N.[Niki],
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A supervised extreme learning committee for food recognition,
CVIU(148), No. 1, 2016, pp. 67-86.
Elsevier DOI
1606
Food recognition
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Tatsuma, A.[Atsushi],
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Modeling Restaurant Context for Food Recognition,
MultMed(19), No. 2, February 2017, pp. 430-440.
IEEE DOI
1702
Which restaurant helps reduce the possible foods.
BibRef
Dehais, J.,
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Mougiakakou, S.,
Two-View 3D Reconstruction for Food Volume Estimation,
MultMed(19), No. 5, May 2017, pp. 1090-1099.
IEEE DOI
1704
Calibration
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Pandey, P.,
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Mandal, B.,
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FoodNet: Recognizing Foods Using Ensemble of Deep Networks,
SPLetters(24), No. 12, December 2017, pp. 1758-1762.
IEEE DOI
1712
convolution, food products, image recognition, neural nets, FoodNet,
Indian food image database, automatic food recognition system,
food recognition
BibRef
Min, W.,
Jiang, S.,
Sang, J.,
Wang, H.,
Liu, X.,
Herranz, L.,
Being a Supercook: Joint Food Attributes and Multimodal Content
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MultMed(19), No. 5, May 2017, pp. 1100-1113.
IEEE DOI
1704
Correlation
BibRef
Min, W.,
Bao, B.K.,
Mei, S.,
Zhu, Y.,
Rui, Y.,
Jiang, S.,
You Are What You Eat: Exploring Rich Recipe Information for
Cross-Region Food Analysis,
MultMed(20), No. 4, April 2018, pp. 950-964.
IEEE DOI
1804
Analytical models, Computers, Cultural differences, Metadata,
Pattern analysis, Probabilistic logic, Visualization,
topic model
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Ege, T.[Takumi],
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Image-Based Food Calorie Estimation Using Recipe Information,
IEICE(E101-D), No. 5, May 2018, pp. 1333-1341.
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1805
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IET-CV(12), No. 3, April 2018, pp. 298-304.
DOI Link
1804
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Heravi, E.J.[Elnaz Jahani],
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An optimized convolutional neural network with bottleneck and spatial
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Elsevier DOI
1804
Food classification, Convolutional neural networks,
Neural network visualization, Deep learning, Spatial pyramid pooling
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Horiguchi, S.,
Amano, S.,
Ogawa, M.,
Aizawa, K.,
Personalized Classifier for Food Image Recognition,
MultMed(20), No. 10, October 2018, pp. 2836-2848.
IEEE DOI
1810
feature extraction, food technology, image classification,
image recognition, class mean classifier,
deep feature
BibRef
Yu, Q.,
Anzawa, M.,
Amano, S.,
Ogawa, M.,
Aizawa, K.,
Food Image Recognition by Personalized Classifier,
ICIP18(171-175)
IEEE DOI
1809
Feature extraction, Image recognition, Optimization, Databases,
Artificial neural networks, Training, Adaptation models,
classifier adaptation
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Ciocca, G.[Gianluigi],
Napoletano, P.[Paolo],
Schettini, R.[Raimondo],
CNN-based features for retrieval and classification of food images,
CVIU(176-177), 2018, pp. 70-77.
Elsevier DOI
1812
Food retrieval, Food dataset, Food recognition, CNN-based features
BibRef
Aguilar, E.,
Remeseiro, B.,
Bolaños, M.,
Radeva, P.,
Grab, Pay, and Eat: Semantic Food Detection for Smart Restaurants,
MultMed(20), No. 12, December 2018, pp. 3266-3275.
IEEE DOI
1812
catering industry, convolution, feedforward neural nets,
food products, image segmentation, object detection,
convolutional neural networks
BibRef
Anzawa, M.[Masashi],
Amano, S.[Sosuke],
Yamakata, Y.[Yoko],
Motonaga, K.[Keiko],
Kamei, A.[Akiko],
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Recognition of Multiple Food Items in A Single Photo for Use in A
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IEICE(E102-D), No. 2, February 2019, pp. 410-414.
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1902
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Bolaños, M.[Marc],
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Regularized uncertainty-based multi-task learning model for food
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JVCIR(60), 2019, pp. 360-370.
Elsevier DOI
1903
Multi-task models, Uncertainty modeling,
Convolutional neural networks, Food image analysis,
Cuisine recognition
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Ege, T.[Takumi],
Yanai, K.[Keiji],
Simultaneous Estimation of Dish Locations and Calories with Multi-Task
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IEICE(E102-D), No. 7, July 2019, pp. 1240-1246.
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1907
BibRef
Earlier:
Simultaneous estimation of food categories and calories with
multi-task CNN,
MVA17(198-201)
DOI Link
1708
Correlation, Estimation, Image recognition, MISO, Organizations,
Standards, Training
BibRef
Shimoda, W.[Wataru],
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Webly-Supervised Food Detection with Foodness Proposal,
IEICE(E102-D), No. 7, July 2019, pp. 1230-1239.
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1907
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Takahashi, K.[Kazuma],
Hattori, T.[Tatsumi],
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Kawanishi, Y.[Yasutomo],
Hirayama, T.[Takatsugu],
Ide, I.[Ichiro],
Deguchi, D.[Daisuke],
Murase, H.[Hiroshi],
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1908
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Cooking With Computers: The Vision of Digital Gastronomy,
PIEEE(107), No. 8, August 2019, pp. 1467-1473.
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1908
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Multi-Scale Multi-Feature Context Modeling for Scene Recognition in
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IP(26), No. 6, June 2017, pp. 2721-2735.
IEEE DOI
1705
BibRef
Earlier:
Joint multi-feature spatial context for scene recognition in the
semantic manifold,
CVPR15(1312-1320)
IEEE DOI
1510
Markov processes, image recognition, neural nets,
Gaussian mixture models, ImageNet, Markov random fields,
co-occurrence patterns, convolutional neural networks,
multiscale multifeature context modeling, optimization problem,
recognition performance, scene recognition, semantic manifold,
semantic space, top-down hierarchical algorithm, Context,
Context modeling, Kernel, Manifolds, Neural networks, Semantics,
Support vector machines, Markov random field, Scene recognition,
context model, convolutional neural networks, multi-scale,
semantic manifold, semantic, multinomial
BibRef
Jiang, S.Q.[Shu-Qiang],
Min, W.Q.[Wei-Qing],
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IP(29), No. 1, 2020, pp. 265-276.
IEEE DOI
1910
convolutional neural nets, feature extraction,
image classification, image fusion, image representation,
convolutional neural networks
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ITS(25), No. 10, October 2024, pp. 13231-13239.
IEEE DOI
2410
Feature extraction, Transformers, Task analysis, Data mining,
Convolutional neural networks, Semantics, Kernel, Crowd counting,
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Anzawa, M.[Masashi],
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2002
Deep learning, Machine learning, Food recognition
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Chua, T.,
Hierarchical Attention Network for Visually-Aware Food Recommendation,
MultMed(22), No. 6, June 2020, pp. 1647-1659.
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2005
Visualization, Recommender systems, Collaboration
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Detection and Recognition of Food in Photo Galleries for Analysis of
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ICIAR20(I:83-94).
Springer DOI
2007
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Uncertainty-aware integration of local and flat classifiers for food
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Elsevier DOI
2008
BibRef
Earlier:
Food Recognition by Integrating Local and Flat Classifiers,
IbPRIA19(I:65-74).
Springer DOI
1910
CNNs, Deep learning, Epistemic uncertainty,
Image classification, Food recognition
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Qaraqe, M.[Marwa],
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IET-IPR(14), No. 11, September 2020, pp. 2469-2479.
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Food Recommendation: Framework, Existing Solutions, and Challenges,
MultMed(22), No. 10, October 2020, pp. 2659-2671.
IEEE DOI
2009
Biomedical monitoring, Diabetes, Heart rate, Temperature sensors,
Analytical models, Blood pressure, Artificial intelligence,
health information management
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Jiji, G.W.[G. Wiselin],
Rajesh, A.,
Food Sustenance Estimation Using Food Image,
IJIG(20), No. 4, October 2020, pp. 2050034.
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Marín, J.[Javier],
Biswas, A.[Aritro],
Ofli, F.[Ferda],
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Salvador, A.[Amaia],
Aytar, Y.[Yusuf],
Weber, I.[Ingmar],
Torralba, A.B.[Antonio B.],
Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking
Recipes and Food Images,
PAMI(43), No. 1, January 2021, pp. 187-203.
IEEE DOI
2012
BibRef
Earlier: A5, A4, A6, A1, A3, A7, A8, Only:
Learning Cross-Modal Embeddings for Cooking Recipes and Food Images,
CVPR17(3068-3076)
IEEE DOI
1711
Task analysis, Semantics, Data models, Search engines,
Neural networks, Deep learning, Cross-modal,
food images.
Data models, Image representation, Semantics, Tools, Training
BibRef
Papadopoulos, D.P.[Dim P.],
Mora, E.[Enrique],
Chepurko, N.[Nadiia],
Huang, K.W.[Kuan Wei],
Ofli, F.[Ferda],
Torralba, A.B.[Antonio B.],
Learning Program Representations for Food Images and Cooking Recipes,
CVPR22(16538-16548)
IEEE DOI
2210
Codes, Image recognition, Semantics, Data models,
Task analysis, Vision + language,
retrieval
BibRef
Chen, J.,
Zhu, B.,
Ngo, C.W.,
Chua, T.S.,
Jiang, Y.G.,
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IP(30), 2021, pp. 1514-1526.
IEEE DOI
2101
Image recognition, Visualization, Phase frequency detectors,
Image segmentation, Fish, Deep learning, Shape, Food images,
deep learning
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Liu, C.X.[Cheng-Xu],
Liang, Y.Z.[Yuan-Zhi],
Xue, Y.[Yao],
Qian, X.M.[Xue-Ming],
Fu, J.L.[Jian-Long],
Food and Ingredient Joint Learning for Fine-Grained Recognition,
CirSysVideo(31), No. 6, June 2021, pp. 2480-2493.
IEEE DOI
2106
Task analysis, Birds, Automobiles, Training, Image recognition, Dogs,
Visualization, Fine-grained, food classification,
joint learning
BibRef
Liang, H.Z.[Hao-Zan],
Wen, G.H.[Gui-Hua],
Hu, Y.[Yang],
Luo, M.N.[Ming-Nan],
Yang, P.[Pei],
Xu, Y.X.[Ying-Xue],
MVANet: Multi-Task Guided Multi-View Attention Network for Chinese
Food Recognition,
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IEEE DOI
2110
Task analysis, Semantics, Feature extraction, Image recognition,
Deep learning, Shape, Fuses, Food recognition,
multi-view attention
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Turan, M.A.T.[M. A. Tugtekin],
Erzin, E.[Engin],
Domain Adaptation for Food Intake Classification With Teacher/Student
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IEEE DOI
2112
Microphones, Sensors, Acoustics, Monitoring, Adaptation models,
Sensor phenomena and characterization, Data models,
throat microphone
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Pan, L.M.[Liang-Ming],
Chen, J.J.[Jing-Jing],
Liu, S.[Shaoteng],
Ngo, C.W.[Chong-Wah],
Kan, M.Y.[Min-Yen],
Chua, T.S.[Tat-Seng],
A Hybrid Approach for Detecting Prerequisite Relations in Multi-Modal
Food Recipes,
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IEEE DOI
2112
Feature extraction, Training, Task analysis, Semantics, Pipelines,
Deep learning, Predictive models, Food recipes, cooking workflow
BibRef
Nguyen, H.T.[Huu-Thanh],
Ngo, C.W.[Chong-Wah],
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SibNet: Food instance counting and segmentation,
PR(124), 2022, pp. 108470.
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2203
Food counting, Food instance segmentation
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Zhu, B.[Bin],
Ngo, C.W.[Chong-Wah],
Chan, W.K.[Wing-Kwong],
Learning From Web Recipe-Image Pairs for Food Recognition: Problem,
Baselines and Performance,
MultMed(24), 2022, pp. 1175-1185.
IEEE DOI
2203
Image recognition, Training, Generative adversarial networks,
Feature extraction, Visualization, Data models, Context modeling,
image-to-image retrieval
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Wang, H.[Hao],
Lin, G.S.[Guo-Sheng],
Hoi, S.C.H.[Steven C.H.],
Miao, C.Y.[Chun-Yan],
Decomposing generation networks with structure prediction for recipe
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PR(126), 2022, pp. 108578.
Elsevier DOI
2204
BibRef
Earlier:
Structure-aware Generation Network for Recipe Generation from Images,
ECCV20(XXVII:359-374).
Springer DOI
2011
Text generation, Vision-and-language
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Deng, L.X.[Li-Xi],
Chen, J.J.[Jing-Jing],
Ngo, C.W.[Chong-Wah],
Sun, Q.R.[Qian-Ru],
Tang, S.[Sheng],
Zhang, Y.D.[Yong-Dong],
Chua, T.S.[Tat-Seng],
Mixed Dish Recognition With Contextual Relation and Domain Alignment,
MultMed(24), No. 2022, pp. 2034-2045.
IEEE DOI
2204
Visualization, Semantics, Feature extraction, Image recognition,
Training, Testing, Context modeling, Mixed dish recognition, Domain alignment
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Wang, H.[Hao],
Sahoo, D.[Doyen],
Liu, C.H.[Cheng-Hao],
Shu, K.[Ke],
Achananuparp, P.[Palakorn],
Lim, E.P.[Ee-Peng],
Hoi, S.C.H.[Steven C. H.],
Cross-Modal Food Retrieval: Learning a Joint Embedding of Food Images
and Recipes With Semantic Consistency and Attention Mechanism,
MultMed(24), 2022, pp. 2515-2525.
IEEE DOI
2205
BibRef
Earlier: A1, A2, A3, A6, A7, Only:
Learning Cross-Modal Embeddings With Adversarial Networks for Cooking
Recipes and Food Images,
CVPR19(11564-11573).
IEEE DOI
2002
Semantics, Task analysis, Data models, Correlation, Visualization,
Training, Sugar, Deep learning, cross-modal retrieval, vision-and-language
BibRef
Wang, Z.L.[Zhi-Ling],
Min, W.Q.[Wei-Qing],
Li, Z.[Zhuo],
Kang, L.P.[Li-Ping],
Wei, X.M.[Xiao-Ming],
Wei, X.L.[Xiao-Lin],
Jiang, S.Q.[Shu-Qiang],
Ingredient-Guided Region Discovery and Relationship Modeling for Food
Category-Ingredient Prediction,
IP(31), 2022, pp. 5214-5226.
IEEE DOI
2208
Visualization, Multitasking, Task analysis, Dictionaries,
Feature extraction, Semantics, Predictive models, deep learning
BibRef
Wang, H.[Hao],
Lin, G.S.[Guo-Sheng],
Hoi, S.C.H.[Steven C. H.],
Miao, C.Y.[Chun-Yan],
Learning Structural Representations for Recipe Generation and Food
Retrieval,
PAMI(45), No. 3, March 2023, pp. 3363-3377.
IEEE DOI
2302
Task analysis, Annotations, Training, Feature extraction, Labeling,
Visualization, Transforms, Text generation, vision-and-language
BibRef
Zhang, M.Y.[Meng-Yang],
Tian, G.H.[Guo-Hui],
Zhang, Y.[Ying],
Liu, H.[Hong],
Sequential Learning for Ingredient Recognition From Images,
CirSysVideo(33), No. 5, May 2023, pp. 2162-2175.
IEEE DOI
2305
Image recognition, Visualization, Cognition, Task analysis,
Image segmentation, Feature extraction, Proposals, cooking logic
BibRef
Min, W.Q.[Wei-Qing],
Wang, Z.L.[Zhi-Ling],
Liu, Y.X.[Yu-Xin],
Luo, M.J.[Meng-Jiang],
Kang, L.P.[Li-Ping],
Wei, X.M.[Xiao-Ming],
Wei, X.L.[Xiao-Lin],
Jiang, S.Q.[Shu-Qiang],
Large Scale Visual Food Recognition,
PAMI(45), No. 8, August 2023, pp. 9932-9949.
IEEE DOI
2307
Image recognition, Visualization, Task analysis, Benchmark testing,
Representation learning, Training, Semantics, Food dataset,
fine-grained recognition
BibRef
Nagarajan, B.[Bhalaji],
Bolaños, M.[Marc],
Aguilar, E.[Eduardo],
Radeva, P.[Petia],
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Elsevier DOI
2309
Knowledge representation, Hard-sample mining, Food recognition,
Deep ensembles, Data augmentation
BibRef
Kaur, R.[Rajdeep],
Kumar, R.[Rakesh],
Gupta, M.[Meenu],
Food Image-based Nutritional Management System to Overcome Polycystic
Ovary Syndrome using DeepLearning: A Systematic Review,
IJIG(23), No. 5 2023, pp. 2350043.
DOI Link
2310
BibRef
Nguyen, H.T.[Huu-Thanh],
Cao, Y.[Yu],
Ngo, C.W.[Chong-Wah],
Chan, W.K.[Wing-Kwong],
FoodMask: Real-time food instance counting, segmentation and
recognition,
PR(146), 2024, pp. 110017.
Elsevier DOI
2311
Food counting, Food instance segmentation, Food recognition
BibRef
Lo, F.P.W.[Frank Po Wen],
Guo, Y.[Yao],
Sun, Y.[Yingnan],
Qiu, J.N.[Jia-Ning],
Lo, B.[Benny],
An Intelligent Vision-Based Nutritional Assessment Method for
Handheld Food Items,
MultMed(25), 2023, pp. 5840-5851.
IEEE DOI
2311
BibRef
Zhou, P.F.[Peng-Fei],
Min, W.Q.[Wei-Qing],
Song, J.J.[Jia-Jun],
Zhang, Y.[Yang],
Jiang, S.Q.[Shu-Qiang],
Synthesizing Knowledge-Enhanced Features for Real-World Zero-Shot
Food Detection,
IP(33), 2024, pp. 1285-1298.
IEEE DOI Code:
WWW Link.
2402
Semantics, Feature extraction, Visualization, Annotations,
Correlation, Training, Task analysis, Food detection, zero-shot learning
BibRef
Su, B.[Binyi],
Zhang, H.[Hua],
Li, J.Z.[Jing-Zhi],
Zhou, Z.[Zhong],
Toward Generalized Few-Shot Open-Set Object Detection,
IP(33), 2024, pp. 1389-1402.
IEEE DOI Code:
WWW Link.
2402
Object detection, Detectors, Training, Task analysis, Solid modeling,
Object recognition, Data models,
unknown decoupling learner
BibRef
Qiu, J.N.[Jia-Ning],
Lo, F.P.W.[Frank P.W.],
Gu, X.[Xiao],
Jobarteh, M.L.[Modou L.],
Jia, W.[Wenyan],
Baranowski, T.[Tom],
Steiner-Asiedu, M.[Matilda],
Anderson, A.K.[Alex K.],
McCrory, M.A.[Megan A.],
Sazonov, E.[Edward],
Sun, M.[Mingui],
Frost, G.[Gary],
Lo, B.[Benny],
Egocentric Image Captioning for Privacy-Preserved Passive Dietary
Intake Monitoring,
Cyber(54), No. 2, February 2024, pp. 679-692.
IEEE DOI
2402
Monitoring, Biomedical monitoring, Cameras, Containers,
Visualization, Volume measurement, Recording, Egocentric vision,
passive dietary intake monitoring
BibRef
Wang, B.[Binglu],
Bu, T.[Tianci],
Hu, Z.[Zaiyi],
Yang, L.[Le],
Zhao, Y.Q.[Yong-Qiang],
Li, X.L.[Xue-Long],
Coarse-to-Fine Nutrition Prediction,
MultMed(26), 2024, pp. 3651-3662.
IEEE DOI
2402
Task analysis, Fats, Smoothing methods, Proteins, Predictive models,
Search problems, Nutrition prediction, coarse-to-fine,
label smoothing
BibRef
Liu, Y.X.[Yu-Xin],
Min, W.Q.[Wei-Qing],
Jiang, S.Q.[Shu-Qiang],
Rui, Y.[Yong],
Convolution-Enhanced Bi-Branch Adaptive Transformer With Cross-Task
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IP(33), 2024, pp. 2572-2586.
IEEE DOI Code:
WWW Link.
2404
Semantics, Visualization, Transformers, Task analysis, Feature extraction,
Image recognition, Fish, Food recognition, multi-label recognition
BibRef
Shukor, M.[Mustafa],
Thome, N.[Nicolas],
Cord, M.[Matthieu],
Vision and Structured-Language Pretraining for Cross-Modal Food
Retrieval,
CVIU(247), 2024, pp. 104071.
Elsevier DOI
2408
Vision Language Pretraining, Foundation models,
Cross-modal retrieval, Computational cooking, Multimodal learning
BibRef
Shin, P.W.[Philip Wootaek],
Sridhar, A.N.[Ajay Narayanan],
Sampson, J.[Jack],
Narayanan, V.[Vijaykrishnan],
A Generative Exploration of Cuisine Transfer,
MTF24(3732-3740)
IEEE DOI
2410
Measurement, Training, Art, Computational modeling, Text to image,
Cuisine Transfer, Text-to-image-editing,
Generative AI Evaluation metrics
BibRef
Vinod, G.[Gautham],
He, J.P.[Jiang-Peng],
Shao, Z.[Zeman],
Zhu, F.Q.[Feng-Qing],
Food Portion Estimation via 3D Object Scaling,
MTF24(3741-3749)
IEEE DOI
2410
Solid modeling, Computational modeling, Neural networks,
Estimation, Training data, Cameras, Food Portion Estimation,
PCA
BibRef
Rodríguez-de-Vera, J.M.[Jesús M.],
Estepa, I.G.[Imanol G.],
Bolaños, M.[Marc],
Nagarajan, B.[Bhalaji],
Radeva, P.[Petia],
LOFI: LOng-tailed FIne-Grained Network for Food Recognition,
MTF24(3750-3760)
IEEE DOI
2410
Training, Representation learning, Computational modeling,
Medical services, Computer architecture, Object detection,
Graph Neural Networks
BibRef
Sharma, A.[Aaryam],
Czarnecki, C.[Chris],
Chen, Y.H.[Yu-Hao],
Xi, P.C.[Peng-Cheng],
Xu, L.L.[Lin-Lin],
Wong, A.[Alexander],
How Much You Ate? Food Portion Estimation on Spoons,
MTF24(3761-3770)
IEEE DOI
2410
Image segmentation, Accuracy, Pipelines, Estimation, Image capture,
Cameras, food, volumetric, estimation, nutrition, computer-vision
BibRef
Alahmari, S.S.[Saeed S.],
Gardner, M.[Michael],
Salem, T.[Tawfiq],
Segment Anything in Food Images,
MTF24(3715-3720)
IEEE DOI
2410
Image segmentation, Adaptation models, Technological innovation,
Accuracy, Image recognition, Image analysis
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Chen, G.Z.[Guang-Zong],
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Sun, M.[Mingui],
Liu, K.[Kangni],
Jia, W.[Wenyan],
Shape-Preserving Generation of Food Images for Automatic Dietary
Assessment,
MTF24(3721-3731)
IEEE DOI
2410
Training, Image recognition, Shape, Image synthesis,
Volume measurement, Neural networks, Training data
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Huang, Y.N.[Yu-Ning],
Hassan, M.A.[M A],
He, J.P.[Jiang-Peng],
Higgins, J.,
McCrory, M.[Megan],
Eicher-Miller, H.[Heather],
Thomas, J.G.[J. Graham],
Sazonov, E.[Edward],
Zhu, F.Q.[Feng-Qing],
Automatic Recognition of Food Ingestion Environment from the AIM-2
Wearable Sensor,
MTF24(3685-3694)
IEEE DOI
2410
Training, Performance evaluation, Scene classification, Accuracy,
Image recognition, Reviews, Neural networks, Dietary Assessment,
Scene Recognition
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Yang, J.[Justin],
Duan, Z.H.[Zhi-Hao],
He, J.P.[Jiang-Peng],
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Learning to Classify New Foods Incrementally Via Compressed Exemplars,
MTF24(3695-3704)
IEEE DOI
2410
Continuing education, Adaptation models, Image coding,
Machine learning, Data models
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Wahed, M.[Muntasir],
Zhou, X.N.[Xiao-Na],
Yu, T.J.[Tian-Jiao],
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Fine-Grained Alignment for Cross-Modal Recipe Retrieval,
WACV24(5572-5581)
IEEE DOI Code:
WWW Link.
2404
Analytical models, Image coding, Codes, Semantics, Robustness,
Task analysis, Algorithms, Vision + language and/or other modalities
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Chhikara, P.[Prateek],
Chaurasia, D.[Dhiraj],
Jiang, Y.F.[Yi-Fan],
Masur, O.[Omkar],
Ilievski, F.[Filip],
FIRE: Food Image to REcipe generation,
WACV24(8169-8179)
IEEE DOI
2404
Computational modeling, Transformers, Decoding,
Intelligent systems, Applications, Food science and nutrition
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Raghavan, S.[Siddeshwar],
He, J.P.[Jiang-Peng],
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Online Class-Incremental Learning For Real-World Food Image
Classification,
WACV24(8180-8189)
IEEE DOI Code:
WWW Link.
2404
Training, Performance evaluation, Economics, Pipelines,
Probabilistic logic, Data models, Applications,
Biomedical / healthcare / medicine
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Saini, N.[Nirat],
Wang, H.Y.[Han-Yu],
Swaminathan, A.[Archana],
Jayasundara, V.[Vinoj],
He, B.[Bo],
Gupta, K.[Kamal],
Shrivastava, A.[Abhinav],
Chop & Learn: Recognizing and Generating Object-State
Compositions,
ICCV23(20190-20201)
IEEE DOI Code:
WWW Link.
2401
Same object in different states.
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Touijer, L.[Larbi],
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Food Image Classification: The Benefit of In-domain Transfer Learning,
CIAP23(II:259-269).
Springer DOI
2312
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He, J.P.[Jiang-Peng],
Zhu, F.Q.[Feng-Qing],
Single-Stage Heavy-Tailed Food Classification,
ICIP23(1115-1119)
IEEE DOI
2312
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Sharma, A.[Aakash],
Czerwinska, K.P.[Katja Pauline],
Johansen, D.[Dag],
Dagenborg, H.[Håvard],
Capturing Nutrition Data for Sports: Challenges and Ethical Issues,
MMMod23(I: 601-612).
Springer DOI
2304
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Akti, S.[Seymanur],
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A Mobile Food Recognition System for Dietary Assessment,
VIAAL22(71-81).
Springer DOI
2208
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He, Y.C.[Yin-Chao],
Hakguder, Z.[Zeynep],
Shi, X.[Xu],
Smart Diet Management Through Food Image and Cooking Recipe Analysis,
VIAAL22(82-93).
Springer DOI
2208
BibRef
Liang, Y.X.[Yu-Xiang],
Li, J.F.[Jiang-Feng],
Zhao, Q.P.[Qin-Pei],
Rao, W.X.[Wei-Xiong],
Zhang, C.X.[Chen-Xi],
Wang, C.[Congrong],
Image Segmentation and Recognition for Multi-Class Chinese Food,
ICIP22(3938-3942)
IEEE DOI
2211
Image segmentation, Image recognition, Image color analysis,
Food Image Segmentation, Food Recognition, Deep Learning
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Shukor, M.[Mustafa],
Couairon, G.[Guillaume],
Grechka, A.[Asya],
Cord, M.[Matthieu],
Transformer Decoders with Multi-Modal Regularization for Cross-Modal
Food Retrieval,
MULA22(4566-4577)
IEEE DOI
2210
Databases, Computational modeling, Transfer learning,
Transformers, Decoding
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Ballús, N.[Nil],
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Opt-SSL: An Enhanced Self-Supervised Framework for Food Recognition,
IbPRIA22(655-666).
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2205
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Hussain, M.[Mazhar],
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User-Biased Food Recognition for Health Monitoring,
CIAP22(III:98-108).
Springer DOI
2205
BibRef
Engstrøm, O.C.G.[Ole-Christian Galbo],
Dreier, E.S.[Erik Schou],
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Predicting Protein Content in Grain Using Hyperspectral Deep Learning,
CVPPA21(1372-1380)
IEEE DOI
2112
Proteins, Deep learning, Convolution,
Data preprocessing, Regression analysis, Convolutional neural networks
BibRef
Chen, F.F.[Fang-Fang],
Xu, X.W.[Xiao-Wei],
Tao, Y.[Ye],
Wang, X.D.[Xiao-Dong],
Wang, Q.C.[Qiu-Chen],
Zhang, S.P.[Shan-Ping],
An Improved Recognition Method Based on YOLOv3-45k for Refrigerator
Item Images,
ICIVC21(85-89)
IEEE DOI
2112
Image recognition, Refrigerators,
Clustering algorithms, Manuals, Feature extraction,
YOLOv3
BibRef
He, J.P.[Jiang-Peng],
Zhu, F.Q.[Feng-Qing],
Online Continual Learning For Visual Food Classification,
LFFAI21(2337-2346)
IEEE DOI
2112
Training, Learning systems, Visualization, Data privacy,
Image databases, Memory management, Clustering algorithms
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Thames, Q.[Quin],
Karpur, A.[Arjun],
Norris, W.[Wade],
Xia, F.T.[Fang-Ting],
Panait, L.[Liviu],
Weyand, T.[Tobias],
Sim, J.[Jack],
Nutrition5k:
Towards Automatic Nutritional Understanding of Generic Food,
CVPR21(8899-8907)
IEEE DOI
2111
Training, Visualization, Technological innovation,
Annotations, Estimation, Streaming media
BibRef
Zhao, H.[Heng],
Yap, K.H.[Kim-Hui],
Kot, A.C.[Alex Chichung],
Fusion Learning using Semantics and Graph Convolutional Network for
Visual Food Recognition,
WACV21(1710-1719)
IEEE DOI
2106
Training, Knowledge engineering, Visualization,
Image recognition, Social networking (online),
BibRef
Ruede, R.[Robin],
Heusser, V.[Verena],
Frank, L.[Lukas],
Roitberg, A.[Alina],
Haurilet, M.[Monica],
Stiefelhagen, R.[Rainer],
Multi-Task Learning for Calorie Prediction on a Novel Large-Scale
Recipe Dataset Enriched with Nutritional Information,
ICPR21(4001-4008)
IEEE DOI
2105
Proteins, Annotations, Neural networks, Estimation, Manuals,
Benchmark testing, calorie estimation, ingredients
BibRef
Lu, Y.[Ya],
Stathopoulou, T.[Thomai],
Mougiakakou, S.[Stavroula],
Partially Supervised Multi-Task Network for Single-View Dietary
Assessment,
ICPR21(8156-8163)
IEEE DOI
2105
Training, Structure from motion,
Volume measurement, Semantics, Pipelines, Estimation
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Li, J.[Jiatong],
Han, F.[Fangda],
Guerrero, R.[Ricardo],
Pavlovic, V.[Vladimir],
Picture-to-Amount (PITA): Predicting Relative Ingredient Amounts from
Food Images,
ICPR21(10343-10350)
IEEE DOI
2105
Deep learning, Measurement, Internet, Task analysis
BibRef
Aguilar, E.[Eduardo],
Nagarajan, B.[Bhalaji],
Khantun, R.[Rupali],
Bolaños, M.[Marc],
Radeva, P.[Petia],
Uncertainty-Aware Data Augmentation for Food Recognition,
ICPR21(4017-4024)
IEEE DOI
2105
Training, Deep learning, Uncertainty,
Image recognition, Computational modeling, Training data
BibRef
Zhou, P.F.[Peng-Fei],
Bai, C.[Cong],
Ying, K.[Kaining],
Xia, J.[Jie],
Huang, L.X.[Li-Xin],
RWMF: A Real-World Multimodal Foodlog Database,
ICPR21(962-968)
IEEE DOI
2105
Databases, Biometrics (access control), Sociology,
Glucose, Diabetes, Classification algorithms
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Lei, J.[Jiabao],
Qiu, J.N.[Jia-Ning],
Lo, F.P.W.[Frank P.-W.],
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Assessing Individual Dietary Intake in Food Sharing Scenarios with Food
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MADiMa20(549-557).
Springer DOI
2103
BibRef
Stanik III, P.[Paul],
Morris, B.T.[Brendan Tran],
Serafica, R.[Reimund],
Webber, K.H.[Kelly Harmon],
Mysnapfoodlog: Culturally Sensitive Food Photo-logging App for Dietary
Biculturalism Studies,
ISVC20(II:470-482).
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2103
BibRef
Okamoto, K.[Kaimu],
Yanai, K.[Keiji],
Uec-foodpix Complete: A Large-scale Food Image Segmentation Dataset,
MADiMa20(647-659).
Springer DOI
2103
BibRef
Pandey, V.[Vaibhav],
Rostami, A.[Ali],
Nag, N.[Nitish],
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Event Mining Driven Context-aware Personal Food Preference Modelling,
MADiMa20(660-676).
Springer DOI
2103
BibRef
Nagarajan, B.[Bhalaji],
Aguilar, E.[Eduardo],
Radeva, P.[Petia],
S2ml-tl Framework for Multi-label Food Recognition,
MADiMa20(629-646).
Springer DOI
2103
BibRef
Mao, R.[Runyu],
He, J.P.[Jiang-Peng],
Shao, Z.[Zeman],
Yarlagadda, S.K.[Sri Kalyan],
Zhu, F.Q.[Feng-Qing],
Visual Aware Hierarchy Based Food Recognition,
MADiMa20(571-598).
Springer DOI
2103
BibRef
Selamat, N.A.[Nur Asmiza],
Ali, S.H.M.[Sawal Hamid Md.],
Analysis of Chewing Signals Based on Chewing Detection Using Proximity
Sensor for Diet Monitoring,
MADiMa20(599-616).
Springer DOI
2103
BibRef
Papathanail, I.[Ioannis],
Lu, Y.[Ya],
Ghosh, A.[Arindam],
Mougiakakou, S.[Stavroula],
Food Recognition in the Presence of Label Noise,
MADiMa20(617-628).
Springer DOI
2103
BibRef
Artese, M.T.[Maria Teresa],
Ciocca, G.[Gianluigi],
Gagliardi, I.[Isabella],
Analysis of Traditional Italian Food Recipes: Experiments and Results,
MADiMa20(677-690).
Springer DOI
2103
BibRef
Shao, H.,
Mu, J.,
Tang, R.,
Chen, X.,
Liu, M.,
Research on Automatic Dish Recognition Algorithm Based on Deep
Learning,
CVIDL20(566-570)
IEEE DOI
2102
catering industry, convolutional neural nets,
feature extraction, image classification, image representation,
Dishes identification
BibRef
Kasturi, S.,
Le Moan, S.,
Bailey, D.,
Smith, J.,
Heating Patterns Recognition in Industrial Microwave-Processed Foods,
IVCNZ20(1-5)
IEEE DOI
2012
Reflectivity, Electromagnetic heating,
Microwave theory and techniques,
kinetics study
BibRef
Gallo, I.[Ignazio],
Ria, G.[Gianmarco],
Landro, N.[Nicola],
La Grassa, R.[Riccardo],
Image and Text fusion for UPMC Food-101 using BERT and CNNs,
IVCNZ20(1-6)
IEEE DOI
2012
Adaptation models, Visualization, Stacking, Bit error rate,
Data models, Proposals, Noise measurement
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Theodoridis, T.,
Solachidis, V.,
Dimitropoulos, K.,
Daras, P.,
A Cross-Modal Variational Framework For Food Image Analysis,
ICIP20(3244-3248)
IEEE DOI
2011
Decoding, Task analysis, Training, Image recognition,
Gaussian distribution, Network architecture,
food analysis
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Han, F.,
Guerrero, R.,
Pavlovic, V.,
CookGAN: Meal Image Synthesis from Ingredients,
WACV20(1439-1447)
IEEE DOI
2006
Feature extraction, Training, Neural networks,
Generators, Image resolution, Computational modeling
BibRef
Fu, H.,
Wu, R.,
Liu, C.,
Sun, J.,
MCEN: Bridging Cross-Modal Gap between Cooking Recipes and Dish
Images with Latent Variable Model,
CVPR20(14558-14568)
IEEE DOI
2008
Training, Task analysis, Visualization, Correlation,
Computational modeling, Computer architecture
BibRef
Sun, J.,
Radecka, K.,
Zilic, Z.,
Exploring Better Food Detection via Transfer Learning,
MVA19(1-6)
DOI Link
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convolutional neural nets, image classification,
learning (artificial intelligence), neural net architecture, Proposals
BibRef
Jelodar, A.B.[Ahmad Babaeian],
Sun, Y.[Yu],
Joint Object and State Recognition Using Language Knowledge,
ICIP19(3352-3356)
IEEE DOI
1910
Cooking related images.
State Classification, Transfer Learning,
joint object and state classification, Concept-Net
BibRef
Salvador, A.[Amaia],
Drozdzal, M.[Michal],
Giro-i-Nieto, X.[Xavier],
Romero, A.[Adriana],
Inverse Cooking: Recipe Generation From Food Images,
CVPR19(10445-10454).
IEEE DOI
2002
BibRef
Konstantinidis, D.[Dimitrios],
Dimitropoulos, K.[Kosmas],
Ioakimidis, I.[Ioannis],
Langlet, B.[Billy],
Daras, P.[Petros],
A Deep Network for Automatic Video-based Food Bite Detection,
CVS19(586-595).
Springer DOI
1912
BibRef
Donadello, I.[Ivan],
Dragoni, M.[Mauro],
Ontology-Driven Food Category Classification in Images,
CIAP19(II:607-617).
Springer DOI
1909
BibRef
Allegra, D.[Dario],
Erba, D.[Daniela],
Farinella, G.M.[Giovanni Maria],
Grazioso, G.[Giovanni],
Maci, P.D.[Paolo Danilo],
Stanco, F.[Filippo],
Tomaselli, V.[Valeria],
Learning to Rank Food Images,
CIAP19(II:629-639).
Springer DOI
1909
BibRef
Bolaños, M.[Marc],
Valdivia, M.[Marc],
Radeva, P.[Petia],
Where and What Am I Eating? Image-Based Food Menu Recognition,
MultLearnApp18(VI:590-605).
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1905
BibRef
Ciocca, G.[Gianluigi],
Mazzini, D.[Davide],
Schettini, R.[Raimondo],
Evaluating CNN-Based Semantic Food Segmentation Across Illuminants,
CCIW19(247-259).
Springer DOI
1905
BibRef
Fang, S.,
Liu, C.,
Tahboub, K.,
Zhu, F.,
Delp, E.J.,
Boushey, C.J.,
cTADA: The Design of a Crowdsourcing Tool for Online Food Image
Identification and Segmentation,
Southwest18(25-28)
IEEE DOI
1809
Image segmentation, Tools, Noise measurement, Crowdsourcing,
Task analysis, Systematics, Training data, Dietary Assessment,
Groundtruth Segmentation
BibRef
Fang, S.,
Shao, Z.,
Mao, R.,
Fu, C.,
Delp, E.J.,
Zhu, F.,
Kerr, D.A.,
Boushey, C.J.,
Single-View Food Portion Estimation: Learning Image-to-Energy
Mappings Using Generative Adversarial Networks,
ICIP18(251-255)
IEEE DOI
1809
Estimation, Image segmentation, Generative adversarial networks,
Task analysis, Image-to-Energy Mapping
BibRef
Chen, H.,
Wang, J.,
Qi, Q.,
Li, Y.,
Sun, H.,
Bilinear CNN Models for Food Recognition,
DICTA17(1-6)
IEEE DOI
1804
feature extraction, feedforward neural nets,
image classification, learning (artificial intelligence),
Image recognition
BibRef
Chen, J.J.[Jing-Jing],
Pang, L.[Lei],
Ngo, C.W.[Chong-Wah],
Cross-Modal Recipe Retrieval: How to Cook this Dish?,
MMMod17(I: 588-600).
Springer DOI
1701
BibRef
Wang, Y.,
Zhu, F.,
Boushey, C.J.,
Delp, E.J.,
Weakly supervised food image segmentation using class activation maps,
ICIP17(1277-1281)
IEEE DOI
1803
Cancer, Image segmentation, Kernel, Semantics, Supervised learning,
Task analysis, Training, dietary assessment, graph model,
weakly supervised learning
BibRef
Ming, Z.Y.[Zhao-Yan],
Chen, J.J.[Jing-Jing],
Cao, Y.[Yu],
Forde, C.[Ciarán],
Ngo, C.W.[Chong-Wah],
Chua, T.S.[Tat Seng],
Food Photo Recognition for Dietary Tracking: System and Experiment,
MMMod18(II:129-141).
Springer DOI
1802
BibRef
Tanno, R.[Ryosuke],
Ege, T.[Takumi],
Yanai, K.[Keiji],
AR DeepCalorieCam: An iOS App for Food Calorie Estimation with
Augmented Reality,
MMMod18(II:352-356).
Springer DOI
1802
BibRef
Christ, P.F.,
Schlecht, S.,
Ettlinger, F.,
Grün, F.,
Heinle, C.,
Tatavatry, S.,
Ahmadi, S.A.,
Diepold, K.,
Menze, B.H.,
Diabetes60: Inferring Bread Units From Food Images Using Fully
Convolutional Neural Networks,
ACVR17(1526-1535)
IEEE DOI
1802
Cameras, Diabetes,
BibRef
Aguilar, E.[Eduardo],
Bolaños, M.[Marc],
Radeva, P.[Petia],
Food Recognition Using Fusion of Classifiers Based on CNNs,
CIAP17(II:213-224).
Springer DOI
1711
BibRef
Razali, M.N.[Mohd Norhisham],
Manshor, N.[Noridayu],
Halin, A.A.[Alfian Abdul],
Yaakob, R.[Razali],
Mustapha, N.[Norwati],
Food Category Recognition Using SURF and MSER Local Feature
Representation,
IVIC17(212-223).
Springer DOI
1711
BibRef
Einarsson, G.[Gudmundur],
Jensen, J.N.[Janus N.],
Paulsen, R.R.[Rasmus R.],
Einarsdottir, H.[Hildur],
Ersbøll, B.K.[Bjarne K.],
Dahl, A.B.[Anders B.],
Christensen, L.B.[Lars Bager],
Foreign Object Detection in Multispectral X-ray Images of Food Items
Using Sparse Discriminant Analysis,
SCIA17(I: 350-361).
Springer DOI
1706
BibRef
Bolaños, M.,
Radeva, P.,
Simultaneous food localization and recognition,
ICPR16(3140-3145)
IEEE DOI
1705
Cameras, Image recognition, Kernel,
Proposals, Training
BibRef
Moulos, I.[Ioannis],
Maramis, C.[Christos],
Ioakimidis, I.[Ioannis],
van den Boer, J.[Janet],
Nolstam, J.[Jenny],
Mars, M.[Monica],
Bergh, C.[Cecilia],
Maglaveras, N.[Nicos],
Objective and Subjective Meal Registration via a Smartphone Application,
MADiMa15(409-416).
Springer DOI
1511
BibRef
Caon, M.[Maurizio],
Carrino, S.[Stefano],
Prinelli, F.[Federica],
Ciociola, V.[Valentina],
Adorni, F.[Fulvio],
Lafortuna, C.[Claudio],
Tabozzi, S.[Sarah],
Serrano, J.[José],
Condon, L.[Laura],
Khaled, O.A.[Omar Abou],
Mugellini, E.[Elena],
Towards an Engaging Mobile Food Record for Teenagers,
MADiMa15(417-424).
Springer DOI
1511
BibRef
Waltner, G.[Georg],
Schwarz, M.[Michael],
Ladstätter, S.[Stefan],
Weber, A.[Anna],
Luley, P.[Patrick],
Bischof, H.[Horst],
Lindschinger, M.[Meinrad],
Schmid, I.[Irene],
Paletta, L.[Lucas],
MANGO: Mobile Augmented Reality with Functional Eating Guidance and
Food Awareness,
MADiMa15(425-432).
Springer DOI
1511
BibRef
Yang, H.X.[Hai-Xiang],
Zhang, D.[Dong],
Lee, D.J.[Dah-Jye],
Huang, M.J.[Min-Jie],
A Sparse Representation Based Classification Algorithm for Chinese Food
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ISVC16(II: 3-10).
Springer DOI
1701
BibRef
Myers, A.,
Johnston, N.,
Rathod, V.,
Korattikara, A.,
Gorban, A.,
Silberman, N.,
Guadarrama, S.,
Papandreou, G.,
Huang, J.,
Murphy, K.,
Im2Calories: Towards an Automated Mobile Vision Food Diary,
ICCV15(1233-1241)
IEEE DOI
1602
Cameras
BibRef
Martinel, N.,
Foresti, G.L.,
Micheloni, C.,
Wide-Slice Residual Networks for Food Recognition,
WACV18(567-576)
IEEE DOI
1806
feature extraction, food technology,
image classification, image representation,
Visualization
BibRef
Martinel, N.,
Piciarelli, C.,
Micheloni, C.,
Foresti, G.L.,
A Structured Committee for Food Recognition,
ACVR15(484-492)
IEEE DOI
1602
Diseases
BibRef
Li, Y.[Ying],
Sheopuri, A.[Anshul],
Applying image analysis to assess food aesthetics and uniqueness,
ICIP15(311-314)
IEEE DOI
1512
Computational aesthetics
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Wang, Y.[Yu],
He, Y.[Ye],
Zhu, F.Q.[Feng-Qing],
Boushey, C.[Carol],
Delp, E.J.[Edward J.],
The Use of Temporal Information in Food Image Analysis,
MADiMa15(317-325).
Springer DOI
1511
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Knez, S.[Simon],
Šajn, L.[Luka],
Food Object Recognition Using a Mobile Device: State of the Art,
MADiMa15(366-374).
Springer DOI
1511
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Pouladzadeh, P.[Parisa],
Yassine, A.[Abdulsalam],
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FooDD: Food Detection Dataset for Calorie Measurement Using Food Images,
MADiMa15(441-448).
Springer DOI
1511
BibRef
Ciocca, G.[Gianluigi],
Napoletano, P.[Paolo],
Schettini, R.[Raimondo],
Food Recognition and Leftover Estimation for Daily Diet Monitoring,
MADiMa15(334-341).
Springer DOI
1511
BibRef
Matsunaga, H.[Hiroki],
Doman, K.[Keisuke],
Hirayama, T.[Takatsugu],
Ide, I.[Ichiro],
Deguchi, D.[Daisuke],
Murase, H.[Hiroshi],
Tastes and Textures Estimation of Foods Based on the Analysis of Its
Ingredients List and Image,
MADiMa15(326-333).
Springer DOI
1511
BibRef
Mazzei, A.[Alessandro],
Anselma, L.[Luca],
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Bolioli, A.[Andrea],
Casuu, M.[Matteo],
Gerbrandy, J.[Jelle],
Lunardi, I.[Ivan],
Mobile Computing and Artificial Intelligence for Diet Management,
MADiMa15(342-349).
Springer DOI
1511
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Highly Accurate Food/Non-Food Image Classification Based on a Deep
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MADiMa15(350-357).
Springer DOI
1511
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Farinella, G.M.[Giovanni Maria],
Moltisanti, M.[Marco],
Battiato, S.[Sebastiano],
Food Recognition Using Consensus Vocabularies,
MADiMa15(384-392).
Springer DOI
1511
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Automatic Expansion of a Food Image Dataset Leveraging Existing
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TASKCV14(3-17).
Springer DOI
1504
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Farinella, G.M.[Giovanni Maria],
Allegra, D.[Dario],
Stanco, F.[Filippo],
Battiato, S.[Sebastiano],
On the Exploitation of One Class Classification to Distinguish Food Vs
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MADiMa15(375-383).
Springer DOI
1511
BibRef
And: A1, A2, A3, Only:
A Benchmark Dataset to Study the Representation of Food Images,
ACVR14(584-599).
Springer DOI
1504
BibRef
Beijbom, O.[Oscar],
Joshi, N.[Neel],
Morris, D.[Dan],
Saponas, S.[Scott],
Khullar, S.[Siddharth],
Menu-Match: Restaurant-Specific Food Logging from Images,
WACV15(844-851)
IEEE DOI
1503
Computer vision
BibRef
Bettadapura, V.[Vinay],
Thomaz, E.[Edison],
Parnami, A.[Aman],
Abowd, G.D.[Gregory D.],
Essa, I.[Irfan],
Leveraging Context to Support Automated Food Recognition in
Restaurants,
WACV15(580-587)
IEEE DOI
1503
Cameras
BibRef
He, Y.[Ye],
Xu, C.[Chang],
Khanna, N.[Nitin],
Boushey, C.J.[Carol J.],
Delp, E.J.[Edward J.],
Analysis of food images: Features and classification,
ICIP14(2744-2748)
IEEE DOI
1502
Accuracy
BibRef
Farinella, G.M.[Giovanni Maria],
Moltisanti, M.[Marco],
Battiato, S.[Sebastiano],
Classifying food images represented as Bag of Textons,
ICIP14(5212-5216)
IEEE DOI
1502
Accuracy
BibRef
Xu, C.[Chang],
He, Y.[Ye],
Khanna, N.[Nitin],
Boushey, C.J.[Carol J.],
Delp, E.J.[Edward J.],
Model-based food volume estimation using 3D pose,
ICIP13(2534-2538)
IEEE DOI
1412
BibRef
Earlier: A2, A1, A3, A4, A5:
Context based food image analysis,
ICIP13(2748-2752)
IEEE DOI
1412
3D model rendering.
Contextual Information
BibRef
Kawano, Y.[Yoshiyuki],
Yanai, K.[Keiji],
Offline 1000-Class Classification on a Smartphone,
IWMV14(193-194)
IEEE DOI
1409
BibRef
Kawano, Y.[Yoshiyuki],
Yanai, K.[Keiji],
FoodCam: A Real-Time Mobile Food Recognition System Employing Fisher
Vector,
MMMod14(II: 369-373).
Springer DOI
1405
BibRef
Earlier:
Rapid Mobile Object Recognition Using Fisher Vector,
ACPR13(476-480)
IEEE DOI
1408
BibRef
And:
Real-Time Mobile Food Recognition System,
IWMV13(1-7)
IEEE DOI
1309
Android application;food recognition;mobile image recognition
image classification.
BibRef
Matsuda, Y.J.[Yu-Ji],
Yanai, K.[Keiji],
Multiple-food recognition considering co-occurrence employing manifold
ranking,
ICPR12(2017-2020).
WWW Link.
1302
BibRef
Joutou, T.[Taichi],
Yanai, K.[Keiji],
A food image recognition system with Multiple Kernel Learning,
ICIP09(285-288).
IEEE DOI
0911
BibRef
Bosch, M.[Marc],
Zhu, F.Q.[Feng-Qing],
Khanna, N.[Nitin],
Boushey, C.J.[Carol J.],
Delp, E.J.[Edward J.],
Combining global and local features for food identification in dietary
assessment,
ICIP11(1789-1792).
IEEE DOI
1201
BibRef
Earlier: A2, A1, A4, A5, Only:
An image analysis system for dietary assessment and evaluation,
ICIP10(1853-1856).
IEEE DOI
1009
Image based.
BibRef
Yang, S.L.[Shulin Lynn],
Chen, M.[Mei],
Pomerleau, D.[Dean],
Sukthankar, R.[Rahul],
Food recognition using statistics of pairwise local features,
CVPR10(2249-2256).
IEEE DOI Video of talk:
WWW Link.
1006
BibRef
Chen, M.[Mei],
Dhingra, K.[Kapil],
Wu, W.[Wen],
Yang, L.[Lei],
Sukthankar, R.[Rahul],
Yang, J.[Jie],
PFID: Pittsburgh fast-food image dataset,
ICIP09(289-292).
IEEE DOI
0911
BibRef
Puri, M.[Manika],
Zhu, Z.W.[Zhi-Wei],
Yu, Q.[Qian],
Divakaran, A.[Ajay],
Sawhney, H.[Harpreet],
Recognition and volume estimation of food intake using a mobile device,
WACV09(1-8).
IEEE DOI
0912
BibRef
Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Weed Detection, Close Range .