{"id":13684766,"url":"https://github.com/AndrewZhuZJU/Awesome-FoodAI","last_synced_at":"2025-05-01T00:32:52.192Z","repository":{"id":217137540,"uuid":"243968730","full_name":"AndrewZhuZJU/Awesome-FoodAI","owner":"AndrewZhuZJU","description":"AI Research Resources in Food 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awesome AI lists","Other Lists"],"sub_categories":["TeX Lists"],"readme":"# Awesome-FoodAI\n\n## Datasets\n- [Food-101](https://www.vision.ee.ethz.ch/datasets_extra/food-101/) (101 food categories)\n- [VireoFood-172](http://vireo.cs.cityu.edu.hk/VireoFood172/) (172 food categories \u0026 353 ingredients)\n- [Recipe1M](http://pic2recipe.csail.mit.edu/) (1M English recipes \u0026 13M food images)\n- [ChineseFoodNet](https://sites.google.com/view/chinesefoodnet/) (208 food categories)\n- [Cookpad](https://dl.acm.org/doi/10.1145/3077136.3080686) (Janpanese recipes \u0026 food images)\n- [YouCook2](http://youcook2.eecs.umich.edu/) (Instructional video)\n- [EPIC-Kitchens](https://epic-kitchens.github.io/2020-100) (Instructional video)\n\n## Recognition\n- [Multi-Scale Multi-View Deep Feature Aggregation for Food Recognition](http://vipl.ict.ac.cn/homepage/jsq/publication/2019-Min-TIP-Multi-scale.pdf) (TIP, 2020)\n- [Zero-shot Ingredient Recognition by Multi-Relational Graph Convolutional Network](http://www.liangmingpan.com/files/publications/AAAI20_Paper.pdf) (AAAI, 2020)\n- [FoodAI: Food Image Recognition via Deep Learning for Smart Food Logging](https://arxiv.org/pdf/1909.11946.pdf) (KDD, 2019)\n- [Mixed-dish Recognition with Contextual Relation Networks](http://staff.ustc.edu.cn/~hexn/papers/mm19-mixed-dish.pdf) (MM, 2019)\n- [Wide-Slice Residual Networks for Food Recognition](https://arxiv.org/abs/1612.06543) (WACV, 2018)\n- [Cross-modal Recipe Retrieval with Rich Food Attributes](http://vireo.cs.cityu.edu.hk/papers/jingjingmm2017.pdf) (MM, 2017)\n- [Deep-based Ingredient Recognition for Cooking Recipe Retrieva](http://vireo.cs.cityu.edu.hk/jingjing/papers/chen2016deep.pdf) (MM, 2016)\n\n## Retrieval\n- [MCEN: Bridging Cross-Modal Gap between Cooking Recipes and Dish Images with Latent Variable Model](https://openaccess.thecvf.com/content_CVPR_2020/papers/Fu_MCEN_Bridging_Cross-Modal_Gap_between_Cooking_Recipes_and_Dish_Images_CVPR_2020_paper.pdf) (CVPR, 2020)\n- [Learning Cross-Modal Embeddings with Adversarial Networks for Cooking Recipes and Food Images](http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Learning_Cross-Modal_Embeddings_With_Adversarial_Networks_for_Cooking_Recipes_and_CVPR_2019_paper.pdf) (CVPR, 2019)[[code](https://github.com/hwang1996/ACME)]\n- [R2GAN: Cross-modal Recipe Retrieval with Generative Adversarial Network](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhu_R2GAN_Cross-Modal_Recipe_Retrieval_With_Generative_Adversarial_Network_CVPR_2019_paper.pdf) (CVPR, 2019)\n- [Cross-Modal Retrieval in the Cooking Context: Learning Semantic Text-Image Embeddings](https://arxiv.org/abs/1804.11146) (SIGIR, 2018) [[code](https://github.com/Cadene/recipe1m.bootstrap.pytorch)]\n- [Deep Understanding of Cooking Procedure for Cross-modal Recipe Retrieval](http://vireo.cs.cityu.edu.hk/papers/2018_p1020-chen.pdf) (MM, 2018)\n- [Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images](http://pic2recipe.csail.mit.edu/tpami19.pdf) (TPAMI, 2019)\n- [Learning Cross-modal Embeddings for Cooking Recipes and Food Images](http://pic2recipe.csail.mit.edu/im2recipe.pdf) (CVPR, 2017) [[code](https://github.com/torralba-lab/im2recipe-Pytorch)]\n- [Cross-modal Recipe Retrieval with Rich Food Attributes](http://vireo.cs.cityu.edu.hk/papers/jingjingmm2017.pdf) (MM, 2017)\n- [Cross-modal Recipe Retrieval: How to Cook This Dish?](http://vireo.cs.cityu.edu.hk/allpage.files/paper_jingjing_MMM17.pdf) (MMM, 2017)\n\n## Generation\n- [CookGAN: Causality based Text-to-Image Synthesis](https://openaccess.thecvf.com/content_CVPR_2020/papers/Zhu_CookGAN_Causality_Based_Text-to-Image_Synthesis_CVPR_2020_paper.pdf) (CVPR, 2020)\n- [CookGAN: Meal Image Synthesis from Ingredients](http://openaccess.thecvf.com/content_WACV_2020/papers/Han_CookGAN_Meal_Image_Synthesis_from_Ingredients_WACV_2020_paper.pdf) (WACV, 2020)\n- [The art of food: Meal image synthesis from ingredients](https://arxiv.org/pdf/1905.13149.pdf)(arxiv, 2019)\n- [Inverse Cooking: Recipe Generation from Food Images](https://arxiv.org/abs/1812.06164) (CVPR, 2019) [[code](https://github.com/facebookresearch/inversecooking)]\n- [How to make a pizza: Learning a compositional layer-based GAN model](http://openaccess.thecvf.com/content_CVPR_2019/papers/Papadopoulos_How_to_Make_a_Pizza_Learning_a_Compositional_Layer-Based_GAN_CVPR_2019_paper.pdf) (CVPR, 2019)\n\n## Instructional Video Analysis\n- [Action Modifiers: Learning from Adverbs in Instructional Videos](https://openaccess.thecvf.com/content_CVPR_2020/papers/Doughty_Action_Modifiers_Learning_From_Adverbs_in_Instructional_Videos_CVPR_2020_paper.pdf) (CVPR, 2020)\n- [Multi-Modal Domain Adaptation for Fine-Grained Action Recognition](https://openaccess.thecvf.com/content_CVPR_2020/papers/Munro_Multi-Modal_Domain_Adaptation_for_Fine-Grained_Action_Recognition_CVPR_2020_paper.pdf) (CVPR, 2020)\n- [DDLSTM: Dual-Domain LSTM for Cross-Dataset Action Recognition](https://openaccess.thecvf.com/content_CVPR_2019/papers/Perrett_DDLSTM_Dual-Domain_LSTM_for_Cross-Dataset_Action_Recognition_CVPR_2019_paper.pdf) (CVPR, 2019)\n- [Towards Automatic Learning of Procedures from Web Instructional Videos](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/17344/16367) (AAAI, 2018)\n- [Scaling Egocentric Vision:The EPIC-KITCHENS Dataset](https://openaccess.thecvf.com/content_ECCV_2018/papers/Dima_Damen_Scaling_Egocentric_Vision_ECCV_2018_paper.pdf) (ECCV, 2018)\n- [CVF Finding “It”: Weakly-Supervised Reference-Aware Visual Grounding in Instructional Videos](https://openaccess.thecvf.com/content_cvpr_2018/papers/Huang_Finding_It_Weakly-Supervised_CVPR_2018_paper.pdf) (CVPR, 2018)\n- [Unsupervised Visual-Linguistic Reference Resolution in Instructional Videos](https://openaccess.thecvf.com/content_cvpr_2017/papers/Huang_Unsupervised_Visual-Linguistic_Reference_CVPR_2017_paper.pdf) (CVPR, 2017)\n\n## Recommendation\n\n## Nutrition Estimation\n\n## Survey\n- [A survey on food computing](https://arxiv.org/abs/1808.07202) (ACM Computing Surveys, 2019)\n\n## Demo\n\n## Contact\nIf you have anything related in FoodAI and want to add in this repo, feel free to contact me at andrewzhu1216@gmail.com. \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FAndrewZhuZJU%2FAwesome-FoodAI","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FAndrewZhuZJU%2FAwesome-FoodAI","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FAndrewZhuZJU%2FAwesome-FoodAI/lists"}