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Food classification is the foundation for developing food vision tasks and plays a key role in the burgeoning field of computational nutrition.
Foodx-251: a dataset for fine-grained food classification
P. Kaur, K. Sikka, W. Wang, S. Belongie, and A. Divakaran · 1907
Earlier work this paper cites.
Support vector machines
M.A. Hearst, S.T. Dumais, E. Osuna, J. Platt, and B. Scholkopf · 1998
Earlier work this paper cites.
Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Pfid: Pittsburgh fast-food image dataset
Mei Chen, Kapil Dhingra, Wen Wu, Lei Yang, Rahul Sukthankar, and Jie Yang · 2009
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A food image recognition system with multiple kernel learning
Taichi Joutou and Keiji Yanai · 2009
Earlier work this paper cites.
A food image recognition system with multiple kernel learning
Taichi Joutou and Keiji Yanai · 2009
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Image recognition of 85 food categories by feature fusion
Hajime Hoashi, Taichi Joutou, and Keiji Yanai · 2010
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Caltech-ucsd birds-200-2011 dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Novel datasets for fine-grained image categorization
E Dataset · 2011
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Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and C. V. Jawahar · 2012
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Multiple-food recognition considering co-occurrence employing manifold ranking
Yuji Matsuda and Keiji Yanai · 2012
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Fine-grained visual classification of aircraft
S. Maji, J. Kannala, E. Rahtu, M. Blaschko, and A. Vedaldi · 2013
Earlier work this paper cites.
Food-101 – mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
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A food recognition system for diabetic patients based on an optimized bag-of-features model
Marios M. Anthimopoulos, Lauro Gianola, Luca Scarnato, Peter Diem, and Stavroula G. Mougiakakou · 2014
Earlier work this paper cites.
Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection
Grant Van Horn, Steve Branson, Ryan Farrell, Scott Haber, Jessie Barry, Panos Ipeirotis, Pietro Perona, and Serge Belongie · 2015
Earlier work this paper cites.
Vehicle type classification using a semisupervised convolutional neural network
Zhen Dong, Yuwei Wu, Mingtao Pei, and Yunde Jia · 2015
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Automatic expansion of a food image dataset leveraging existing categories with domain adaptation
Yoshiyuki Kawano and Keiji Yanai · 2015
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Recipe recognition with large multimodal food dataset
Xin Wang, Devinder Kumar, Nicolas Thome, Matthieu Cord, and Frédéric Precioso · 2015
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Geolocalized modeling for dish recognition
Ruihan Xu, Luis Herranz, Shuqiang Jiang, Shuang Wang, Xinhang Song, and Ramesh Jain · 2015
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A benchmark dataset to study the representation of food images
Giovanni Maria Farinella, Dario Allegra, and Filippo Stanco · 2015
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Real-time food intake classification and energy expenditure estimation on a mobile device
Daniele Ravì, Benny Lo, and Guang-Zhong Yang · 2015
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Very deep convolutional networks for large-scale image recognition
K Simonyan and A Zisserman · 2015
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Food/non-food image classification and food categorization using pre-trained googlenet model
Ashutosh Singla, Lin Yuan, and Touradj Ebrahimi · 2016
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Deep-based ingredient recognition for cooking recipe retrieval
Jingjing Chen and Chong-wah Ngo · 2016
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Fine-grained image classification by exploring bipartite-graph labels
Feng Zhou and Yuanqing Lin · 2016
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Snap, eat, repeat: A food recognition engine for dietary logging
Michele Merler, Hui Wu, Rosario Uceda-Sosa, Quoc-Bao Nguyen, and John R. Smith · 2016
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Retrieval and classification of food images
Giovanni Maria Farinella, Dario Allegra, Marco Moltisanti, Filippo Stanco, and Sebastiano Battiato · 2016
Swin transformer: Hierarchical vision transformer using shifted windows
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo · 2021
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Malaysian food recognition using alexnet cnn and transfer learning
Rafhan Amnani Rahmat and Suhaili Beeran Kutty · 2021
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Combining recurrent, convolutional, and continuous-time models with linear state space layers
Albert Gu, Isys Johnson, Karan Goel, Khaled Kamal Saab, Tri Dao, Atri Rudra, and Christopher Re · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Cnfood-241
Bokun Fan · 2022
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Cited alongside, same era.
Food image recognition using very deep convolutional networks
Hamid Hassannejad, Guido Matrella, Paolo Ciampolini, Ilaria De Munari, Monica Mordonini, and Stefano Cagnoni · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alex Alemi · 2016
Cited alongside, same era.
Chinesefoodnet: A large-scale image dataset for chinese food recognition
Xin Chen, Hua Zhou, Yu Zhu, and Liang Diao · 2017
Cited alongside, same era.
Learning cnn-based features for retrieval of food images
Gianluigi Ciocca, Paolo Napoletano, and Raimondo Schettini · 2017
Cited alongside, same era.
Vegfru: A domain-specific dataset for fine-grained visual categorization
Saihui Hou, Yushan Feng, and Zilei Wang · 2017
Cited alongside, same era.
Mohanty SP, Singhal G, Scuccimarra EA, Kebaili D, Héritier H, Boulanger V, and Salathé M · 2022
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A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
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Food classification using transfer learning technique
VijayaKumari G., Priyanka Vutkur, and Vishwanath P · 2022
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Efficiently modeling long sequences with structured state spaces
Albert Gu, Karan Goel, and Christopher Re · 2022
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Diagonal state spaces are as effective as structured state spaces
Ankit Gupta, Albert Gu, and Jonathan Berant · 2022
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Automatic chinese food recognition based on a stacking fusion model
Bokun Fan, Weiqi Li, Liang Dong, Jingzhen Li, and Zedong Nie · 2023
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Large scale visual food recognition
Weiqing Min, Zhiling Wang, Yuxin Liu, Mengjiang Luo, Liping Kang, Xiaoming Wei, Xiaolin Wei, and Shuqiang Jiang · 2023
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Learn from each other to classify better: Cross-layer mutual attention learning for fine-grained visual classification
Dichao Liu, Longjiao Zhao, Yu Wang, and Jien Kato · 2023
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Repvit: Revisiting mobile cnn from vit perspective
Ao Wang, Hui Chen, Zijia Lin, Jungong Han, and Guiguang Ding · 2023
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Mamba: Linear-time sequence modeling with selective state spaces
Tri Dao Albert Gu · 2023
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Taiwanese-food-101
Tsan-Lun Yang · 2024
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Vision mamba: Efficient visual representation learning with bidirectional state space model
Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, and Xinggang Wang · 2024
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U-mamba: Enhancing long-range dependency for biomedical image segmentation
Bo Wang Jun Ma, Feifei Li · 2024
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Vm-unet: Vision mamba unet for medical image segmentation
Suncheng Xiang Jiacheng Ruan · 2024
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nnmamba: 3d biomedical image segmentation, classification and landmark detection with state space model
Haifan Gong, Luoyao Kang, Yitao Wang, Xiang Wan, and Haofeng Li · 2024
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Mambamorph: a mamba-based backbone with contrastive feature learning for deformable mr-ct registration
Cai Meng Tao Guo, Yinuo Wang · 2024
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Vivim: a video vision mamba for medical video object segmentation
Lei Zhu Yijun Yang, Zhaohu Xing · 2024
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