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People enjoy food photography because they appreciate food.
CHEF: A model of case-based planning
Kristian J. Hammond · 1986
Earlier work this paper cites.
Food, self and identity
Claude Fischler · 1988
Earlier work this paper cites.
A learning algorithm for continually running fully recurrent neural networks
Ronald J. Williams and David Zipser · 1989
Earlier work this paper cites.
Random k-labelsets: An ensemble method for multilabel classification
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Food-101–mining discriminative components with random forests
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Adam: A method for stochastic optimization
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Flow graph corpus from recipe texts
Shinsuke Mori, Hirokuni Maeta, Yoko Yamakata, and Tetsuro Sasada · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
Earlier work this paper cites.
CNN: single-label to multi-label
Yunchao Wei, Wei Xia, Junshi Huang, Bingbing Ni, Jian Dong, Yao Zhao, and Shuicheng Yan · 2014
Earlier work this paper cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
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Earlier work this paper cites.
Deep visual-semantic alignments for generating image descriptions
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Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
Earlier work this paper cites.
Im2calories: towards an automated mobile vision food diary
Austin Meyers, Nick Johnston, Vivek Rathod, Anoop Korattikara, Alex Gorban, Nathan Silberman, Sergio Guadarrama, George Papandreou, Jonathan Huang, and Kevin P Murphy · 2015
Earlier work this paper cites.
Faster R-CNN: towards real-time object detection with region proposal networks
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Earlier work this paper cites.
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Recipe recognition with large multimodal food dataset
Xin Wang, Devinder Kumar, Nicolas Thome, Matthieu Cord, and Frederic Precioso · 2015
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Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio · 2015
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Geolocalized modeling for dish recognition
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Deep residual learning for image recognition
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Nutrinet: A deep learning food and drink image recognition system for dietary assessment
Simon Mezgec and Barbara Koroušić Seljak · 2017
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Maximizing subset accuracy with recurrent neural networks in multi-label classification
Jinseok Nam, Eneldo Loza Mencía, Hyunwoo J Kim, and Johannes Fürnkranz · 2017
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Deep learning for food recognition
Chong-Wah Ngo · 2017
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Is saki# delicious?: The food perception gap on instagram and its relation to health
Ferda Ofli, Yusuf Aytar, Ingmar Weber, Raggi al Hammouri, and Antonio Torralba · 2017
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Automatic differentiation in pytorch
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Later among the works it cites.
Deepsetnet: Predicting sets with deep neural networks
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Globally coherent text generation with neural checklist models
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Deepfood: Deep learning-based food image recognition for computer-aided dietary assessment
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Order matters: Sequence to sequence for sets
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CNN-RNN: A unified framework for multi-label image classification
Jiang Wang, Yi Yang, Junhua Mao, Zhiheng Huang, Chang Huang, and Wei Xu · 2016
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Chinese poetry generation with planning based neural network
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S Hamid Rezatofighi, Anton Milan, Ehsan Abbasnejad, Anthony Dick, Ian Reid, et al · 2017
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Learning cross-modal embeddings for cooking recipes and food images
Amaia Salvador, Nicholas Hynes, Yusuf Aytar, Javier Marin, Ferda Ofli, Ingmar Weber, and Antonio Torralba · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Learning deep latent spaces for multi-label classification
Chih-Kuan Yeh, Wei-Chieh Wu, Wei-Jen Ko, and Yu-Chiang Frank Wang · 2017
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Cross-modal retrieval in the cooking context: Learning semantic text-image embeddings
Micael Carvalho, Rémi Cadène, David Picard, Laure Soulier, Nicolas Thome, and Matthieu Cord · 2018
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Joint learning of set cardinality and state distribution
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Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
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