A Large-Scale Benchmark for Food Image Segmentation
暂无分享,去创建一个
Ee-Peng Lim | Xiongwei Wu | Steven C.H. Hoi | Qianru Sun | Ying Liu | Xin Fu | S. Hoi | Ee-Peng Lim | Qianru Sun | Xiongwei Wu | Ying Liu | Xin Fu
[1] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[2] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[4] Shuang Wang,et al. Geolocalized Modeling for Dish Recognition , 2015, IEEE Transactions on Multimedia.
[5] Hedy Kober,et al. Training in cognitive strategies reduces eating and improves food choice , 2018, Proceedings of the National Academy of Sciences.
[6] Kaiming He,et al. Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[7] Paolo Napoletano,et al. Learning CNN-based Features for Retrieval of Food Images , 2017, ICIAP Workshops.
[8] Jeffrey Dean,et al. Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.
[9] Yunchao Wei,et al. CCNet: Criss-Cross Attention for Semantic Segmentation , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[10] Arjun Karpur,et al. Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[11] Feng Zhou,et al. Fine-Grained Image Classification by Exploring Bipartite-Graph Labels , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Tat-Seng Chua,et al. Mixed-dish Recognition with Contextual Relation Networks , 2019, ACM Multimedia.
[13] Zhiling Wang,et al. ISIA Food-500: A Dataset for Large-Scale Food Recognition via Stacked Global-Local Attention Network , 2020, ACM Multimedia.
[14] Xiaogang Wang,et al. Pyramid Scene Parsing Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Kaiming He,et al. Panoptic Feature Pyramid Networks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Lei Yang,et al. PFID: Pittsburgh fast-food image dataset , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).
[17] Chong-Wah Ngo,et al. Mixed Dish Recognition through Multi-Label Learning , 2019, CEA@ICMR.
[18] Giovanni Maria Farinella,et al. A Benchmark Dataset to Study the Representation of Food Images , 2014, ECCV Workshops.
[19] Keiji Yanai,et al. UEC-FoodPix Complete: A Large-Scale Food Image Segmentation Dataset , 2020, ICPR Workshops.
[20] Shuqiang Jiang,et al. Ingredient-Guided Cascaded Multi-Attention Network for Food Recognition , 2019, ACM Multimedia.
[21] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[22] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[23] Keiji Yanai,et al. Multiple-food recognition considering co-occurrence employing manifold ranking , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).
[24] Alexei A. Efros,et al. Unbiased look at dataset bias , 2011, CVPR 2011.
[25] Keiji Yanai,et al. Automatic Expansion of a Food Image Dataset Leveraging Existing Categories with Domain Adaptation , 2014, ECCV Workshops.
[26] Philips Kokoh Prasetyo,et al. RecipeGPT: Generative Pre-training Based Cooking Recipe Generation and Evaluation System , 2020, WWW.
[27] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[28] Amaia Salvador,et al. Learning Cross-Modal Embeddings for Cooking Recipes and Food Images , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[29] Antonio Torralba,et al. Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images , 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[30] Amaia Salvador,et al. Inverse Cooking: Recipe Generation From Food Images , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Abhinav Gupta,et al. Non-local Neural Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[32] D. Tilman,et al. Global diets link environmental sustainability and human health , 2014, Nature.
[33] Zhang Hanwang,et al. Self-Regulation for Semantic Segmentation , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[34] Ling Shao,et al. Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions , 2021, ArXiv.
[35] Tao Xiang,et al. Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[36] Wataru Shimoda,et al. Learning Food Image Similarity for Food Image Retrieval , 2017, 2017 IEEE Third International Conference on Multimedia Big Data (BigMM).
[37] Zhenguang Liu,et al. Combining Graph Neural Networks With Expert Knowledge for Smart Contract Vulnerability Detection , 2021, IEEE Transactions on Knowledge and Data Engineering.
[38] Steven C. H. Hoi,et al. FoodAI: Food Image Recognition via Deep Learning for Smart Food Logging , 2019, KDD.
[39] Matthieu Guillaumin,et al. Food-101 - Mining Discriminative Components with Random Forests , 2014, ECCV.
[40] Chunyan Miao,et al. Structure-Aware Generation Network for Recipe Generation from Images , 2020, ECCV.
[41] Sergio Guadarrama,et al. Im2Calories: Towards an Automated Mobile Vision Food Diary , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[42] Marios Anthimopoulos,et al. A Food Recognition System for Diabetic Patients Based on an Optimized Bag-of-Features Model , 2014, IEEE Journal of Biomedical and Health Informatics.
[43] John R. Smith,et al. Snap, Eat, RepEat: A Food Recognition Engine for Dietary Logging , 2016, MADiMa @ ACM Multimedia.
[44] Siyao Wang,et al. Mining Discriminative Food Regions for Accurate Food Recognition , 2019, BMVC.
[45] Stephen Lin,et al. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[46] Chong-Wah Ngo,et al. Deep-based Ingredient Recognition for Cooking Recipe Retrieval , 2016, ACM Multimedia.
[47] Keiji Yanai,et al. Image Recognition of 85 Food Categories by Feature Fusion , 2010, 2010 IEEE International Symposium on Multimedia.
[48] Matthieu Cord,et al. Recipe recognition with large multimodal food dataset , 2015, 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW).
[49] Ajay Divakaran,et al. FoodX-251: A Dataset for Fine-grained Food Classification , 2019, ArXiv.
[50] Wataru Shimoda,et al. A New Large-scale Food Image Segmentation Dataset and Its Application to Food Calorie Estimation Based on Grains of Rice , 2019, MADiMa @ ACM Multimedia.
[51] Iasonas Kokkinos,et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[52] Iasonas Kokkinos,et al. Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs , 2014, ICLR.
[53] Keiji Yanai,et al. A food image recognition system with Multiple Kernel Learning , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).
[54] Jinhui Tang,et al. Feature Pyramid Transformer , 2020, ECCV.
[55] Yuning Jiang,et al. Unified Perceptual Parsing for Scene Understanding , 2018, ECCV.
[56] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[57] Wen Tang,et al. MUSEFood: Multi-Sensor-Based Food Volume Estimation on Smartphones , 2019, 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI).
[58] Touradj Ebrahimi,et al. Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model , 2016, MADiMa @ ACM Multimedia.
[59] Georg Heigold,et al. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale , 2021, ICLR.
[60] Sebastian Ramos,et al. The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).