Caltech-UCSD Birds

Caltech-UCSD Birds 200. Caltech-UCSD Birds 200 (CUB-200) is an image dataset with photos of 200 bird species (mostly North American). For detailed information about the dataset, please see the technical report linked below. Number of categories: 200; Number of images: 6,033; Annotations: Bounding Box, Rough Segmentation, Attributes. Some related datasets are Caltech-256, the Oxford Flower Dataset, and Animals with Attributes. More datasets are available at the Caltech Vision Dataset Archive.


References in zbMATH (referenced in 19 articles )

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  1. Rudin, Cynthia; Chen, Chaofan; Chen, Zhi; Huang, Haiyang; Semenova, Lesia; Zhong, Chudi: Interpretable machine learning: fundamental principles and 10 grand challenges (2022)
  2. Koo, Bongyeong; Choi, Han-Soo; Kang, Myungjoo: Simple feature pyramid network for weakly supervised object localization using multi-scale information (2021)
  3. Qi, Zhongang; Khorram, Saeed; Fuxin, Li: Embedding deep networks into visual explanations (2021)
  4. Roads, Brett D.; Mozer, Michael C.: Predicting the ease of human category learning using radial basis function networks (2021)
  5. Ye, Han-Jia; Hu, Hexiang; Zhan, De-Chuan: Learning adaptive classifiers synthesis for generalized few-shot learning (2021)
  6. Benyi Hu, Ren-Jie Song, Xiu-Shen Wei, Yazhou Yao, Xian-Sheng Hua, Yuehu Liu: PyRetri: A PyTorch-based Library for Unsupervised Image Retrieval by Deep Convolutional Neural Networks (2020) arXiv
  7. Hu, Xin; Liu, Jun; Ma, Jie; Pan, Yudai; Zhang, Lingling: Fine-grained 3D-attention prototypes for few-shot learning (2020)
  8. Li, Aoxue; Lu, Zhiwu; Guan, Jiechao; Xiang, Tao; Wang, Liwei; Wen, Ji-Rong: Transferrable feature and projection learning with class hierarchy for zero-shot learning (2020)
  9. Xu, Xiaofeng; Tsang, Ivor W.; Liu, Chuancai: Improving generalization via attribute selection on out-of-the-box data (2020)
  10. Ye, Han-Jia; Sheng, Xiang-Rong; Zhan, De-Chuan: Few-shot learning with adaptively initialized task optimizer: a practical meta-learning approach (2020)
  11. Yao, Xin; Huang, Tianchi; Wu, Chenglei; Zhang, Rui-Xiao; Sun, Lifeng: Adversarial feature alignment: avoid catastrophic forgetting in incremental task lifelong learning (2019)
  12. Francis, Deena P.; Raimond, Kumudha: An improvement of the parameterized frequent directions algorithm (2018)
  13. Zhang, Lingling; Liu, Jun; Luo, Minnan; Chang, Xiaojun; Zheng, Qinghua: Deep semisupervised zero-shot learning with maximum mean discrepancy (2018)
  14. Wang, Qian; Chen, Ke: Zero-shot visual recognition via bidirectional latent embedding (2017)
  15. Ghashami, Mina; Liberty, Edo; Phillips, Jeff M.; Woodruff, David P.: Frequent directions: simple and deterministic matrix sketching (2016)
  16. Joshi, Shalmali; Ghosh, Joydeep; Reid, Mark; Koyejo, Oluwasanmi: Rényi divergence minimization based co-regularized multiview clustering (2016)
  17. Branson, Steve; Van Horn, Grant; Wah, Catherine; Perona, Pietro; Belongie, Serge: The ignorant led by the blind: a hybrid human-machine vision system for fine-grained categorization (2014)
  18. Maji, Subhransu; Shakhnarovich, Gregory: Part and attribute discovery from relative annotations (2014) ioport
  19. Tousch, Anne-Marie; Herbin, Stéphane; Audibert, Jean-Yves: Semantic hierarchies for image annotation: a survey (2012) ioport