LabelMe: A Database and Web-Based Tool for Image Annotation. We seek to build a large collection of images with ground truth labels to be used for object detection and recognition research. Such data is useful for supervised learning and quantitative evaluation. To achieve this, we developed a web-based tool that allows easy image annotation and instant sharing of such annotations. Using this annotation tool, we have collected a large dataset that spans many object categories, often containing multiple instances over a wide variety of images. We quantify the contents of the dataset and compare against existing state of the art datasets used for object recognition and detection. Also, we show how to extend the dataset to automatically enhance object labels with WordNet, discover object parts, recover a depth ordering of objects in a scene, and increase the number of labels using minimal user supervision and images from the web

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  1. Marcos Nieto, Orti Senderos, Oihana Otaegui: Boosting AI applications: Labeling format for complex datasets (2021) not zbMATH
  2. Yang, Cong; Wang, Wenfeng; Zhang, Yunhui; Zhang, Zhikai; Shen, Lina; Li, Yipeng; See, John: MLife: a lite framework for machine learning lifecycle initialization (2021)
  3. Liu, Li; Ouyang, Wanli; Wang, Xiaogang; Fieguth, Paul; Chen, Jie; Liu, Xinwang; Pietikäinen, Matti: Deep learning for generic object detection: a survey (2020)
  4. Lüddecke, Timo; Agostini, Alejandro; Fauth, Michael; Tamosiunaite, Minija; Wörgötter, Florentin: Distributional semantics of objects in visual scenes in comparison to text (2019)
  5. Liu, Yang; Feng, Lin; Liu, Shenglan; Sun, Muxin: Global similarity preserving hashing (2018)
  6. Zhu, Zhihui; Li, Gang; Ding, Jiajun; Li, Qiuwei; He, Xiongxiong: On collaborative compressive sensing systems: the framework, design, and algorithm (2018)
  7. Kim, Minyoung: Efficient histogram dictionary learning for text/image modeling and classification (2017)
  8. Eiter, Thomas; Kaminski, Tobias: Exploiting contextual knowledge for hybrid classification of visual objects (2016)
  9. Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, Bernt Schiele: The Cityscapes Dataset for Semantic Urban Scene Understanding (2016) arXiv
  10. Borji, Ali: What is a salient object? A dataset and a baseline model for salient object detection (2015)
  11. Le, Tam; Cuturi, Marco: Adaptive Euclidean maps for histograms: generalized Aitchison embeddings (2015)
  12. Bottou, Léon: From machine learning to machine reasoning (2014) ioport
  13. Eslami, S. M. Ali; Heess, Nicolas; Williams, Christopher K. I.; Winn, John: The shape Boltzmann machine: a strong model of object shape (2014)
  14. Gong, Boqing; Grauman, Kristen; Sha, Fei: Learning kernels for unsupervised domain adaptation with applications to visual object recognition (2014)
  15. Kapoor, Ashish; Caicedo, Juan C.; Lischinski, Dani; Kang, Sing Bing: Collaborative personalization of image enhancement (2014) ioport
  16. Li, Li-Jia; Su, Hao; Lim, Yongwhan; Fei-Fei, Li: Object bank: an object-level image representation for high-level visual recognition (2014) ioport
  17. Mesnil, Grégoire; Bordes, Antoine; Weston, Jason; Chechik, Gal; Bengio, Yoshua: Learning semantic representations of objects and their parts (2014)
  18. Patterson, Genevieve; Xu, Chen; Su, Hang; Hays, James: The SUN attribute database: beyond categories for deeper scene understanding (2014) ioport
  19. Vijayanarasimhan, Sudheendra; Grauman, Kristen: Large-scale live active learning: training object detectors with crawled data and crowds (2014) ioport
  20. Payet, Nadia; Todorovic, Sinisa: SLEDGE: sequential labeling of image edges for boundary detection (2013)

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