Open Access
ITM Web Conf.
Volume 40, 2021
International Conference on Automation, Computing and Communication 2021 (ICACC-2021)
Article Number 02001
Number of page(s) 6
Section Communication
Published online 09 August 2021
  1. Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, Eftychios Protopapadakis, “Deep Learning for Computer Vision: A Brief Review”, Computational Intelligence and Neuroscience, vol. 2018 [Google Scholar]
  2. A Data Geek’s Guide to Recognize Landmarks [Google Scholar]
  3. Visual Learning for Landmark Recognition [Google Scholar]
  4. Instance-level Recognition [Google Scholar]
  5. A. Kausar, M. Sharif, J. Park, and D. R. Shin, “Pure-CNN: A Framework for Fruit Images Classification,” 2018 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 2018,pp.404–408. [Google Scholar]
  6. Andrei Boiarov and Eduard Tyantov. 2019. Large Scale Landmark Recognition via Deep Metric Learning. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management Association for Computing Machinery, New York, NY, USA, 169–178. [Google Scholar]
  7. I. Kuzborskij, F. M. Carlucci and B. Caputo, “When Naive Bayes Nearest Neighbors Meet Convolutional Neural Networks,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 2016, pp. 2100–2109 [Google Scholar]
  8. Mahbub Hussain, Jordan Bird, and Diego Faria, “A Study on CNN Transfer Learning for Image Classification” at School of Engineering Aston University,Birmingham. [Google Scholar]
  9. Hyeonwoo Noh, Andre Araujo, Jack Sim, Tobias Weyand, and Bohyung Han, “Large-Scale Image Retrieval with Attentive Deep Local Features” [Google Scholar]
  10. A Comprehensive Hands-on Guide to Transfer Learning with Real-World Applications in Deep Learning. [Google Scholar]
  11. Google-Landmarks Dataset [Google Scholar]

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