ImageNet Large-Scale Visual Recognition Challenge
Keywords:
ImageNet, Large-Scale, Visual RecognitionAbstract
The ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) is a pivotal benchmark in computer vision that has significantly advanced the fields of image classification and object detection. By providing a large-scale dataset and standardized evaluation protocols, it enables consistent comparison and drives innovation in visual recognition algorithms. The primary objective of ILSVRC is to evaluate and improve the accuracy and efficiency of algorithms on large-scale visual recognition tasks. Deep convolutional neural networks (CNNs) and related deep learning methods have been the dominant approaches employed throughout the challenge, evolving in complexity and performance over time. The results demonstrate substantial reductions in error rates and marked improvements in recognition capabilities. In conclusion, ILSVRC has catalyzed progress toward achieving human-level performance in visual perception and recognition, influencing both academic research and practical AI applications.
Downloads
References
Sarker, I.H. Machine learning: Algorithms, real-world applications and research directions. SN Comput. Sci. 2021, 2, 160.
Cioffi, R.; Travaglioni, M.; Piscitelli, G.; Petrillo, A.; De Felice, F. Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions. Sustainability 2020, 12, 492.
Xu, Y.; Lu, C.; Zhang, J.; Peng, Z.; Zhou, Y. Artificial intelligence: A powerful paradigm for scientific research. Innovation 2021, 2, 100179.
Acevedo, A.; Merino, A.; Alférez, S.; Molina, Á.; Boldú, L.; Rodellar, J. A dataset of microscopic peripheral blood cell images for development of automatic recognition systems. Data Brief 2020, 30, 105474.
Alzubaidi, L.; Zhang, J.; Humaidi, A.J.; Al-Dujaili, A.; Duan, Y.; Al-Shamma, O.; Farhan, L.; Al-Amidie, M.; Santamaría, J. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. J. Big Data 2021, 8, 53.
Albawi, S.; Mohammed, T.A.; Al-Zawi, S. Understanding of a convolutional neural network. In Proceedings of the International Conference on Engineering and Technology (ICET), Antalya, Turkey, 21–23 August 2017; pp. 1–6.
Yuan, Y.; Fang, J.; Lu, X.; Feng, Y. Remote sensing image scene classification using rearranged local features. IEEE Trans. Geosci. Remote Sens. 2018, 57, 1779–1792.
He, N.; Fang, L.; Li, S.; Plaza, A.; Plaza, J. Remote sensing scene classification using multilayer stacked covariance pooling. IEEE Trans. Geosci. Remote Sens. 2018, 56, 6899–6910.
Lu, X.; Sun, H.; Zheng, X. A feature aggregation convolutional neural network for remote sensing scene classification. IEEE Trans. Geosci. Remote Sens. 2019, 57, 7894–7906.
Minetto, R.; Segundo, M.P.; Sarkar, S. Hydra: An ensemble of convolutional neural networks for geospatial land classification. IEEE Trans. Geosci. Remote Sens. 2019, 57, 6530–6541.
Wang, Q.; Liu, S.; Chanussot, J.; Li, X. Scene classification with recurrent attention of VHR remote sensing images. IEEE Trans. Geosci. Remote Sens. 2018, 57, 1155–1167.
Zelener, A. Object Localization, Segmentation, and Classification in 3D Images. Ph.D. Thesis, The City University of New York, New York, NY, USA, 2018.
Chen, X.; Kundu, K.; Zhang, Z.; Ma, H.; Fidler, S.; Urtasun, R. Monocular 3d object detection for autonomous driving. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 2147–2156
Dewi, C.; Chen, R.C.; Yu, H.; Jiang, X. Robust detection method for improving small traffic sign recognition based on spatial pyramid pooling. J. Ambient. Intell. Humaniz. Comput. 2021, 1–18.
Sharma, V.; Mir, R.N. A comprehensive and systematic look up into deep learning based object detection techniques: A review. Comput. Sci. Rev. 2020, 38, 100301.
Masita, K.L.; Hasan, A.N.; Shongwe, T. Deep Learning in Object Detection: A Review. In Proceedings of the 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), Durban, South Africa, 6–7 August 2020; pp. 1–11.
Poyser, M.; Atapour-Abarghouei, A.; Breckon, T.P. On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures. In Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, 10–15 January 2021; pp. 2830–2837.
Yang, E.H.; Amer, H.; Jiang, Y. Compression Helps Deep Learning in Image Classification. Entropy 2021, 23, 881.
Signorelli, C.M. Can computers become conscious and overcome humans? Front. Robot. AI 2018, 5, 121.
Krauss, P.; Maier, A. Will we ever have conscious machines? Front. Comput. Neurosci. 2020, 14, 556544.
Kim, Y.; Lee, H.J.; Shim, J. Developing data-conscious deep learning models for product classification. Appl. Sci. 2021, 11, 5694.
Pepperell, A.R. Does machine understanding require consciousness? Front. Syst. Neurosci. 2022, 16, 788486.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
