Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Shohei Enomoto"'
Publikováno v:
IEEE Access, Vol 12, Pp 149593-149605 (2024)
Distribution shifts, which often occur in the real world, degrade the accuracy of deep learning systems, and thus improving robustness to distribution shifts is essential for practical applications. To improve robustness, we study an image enhancemen
Externí odkaz:
https://doaj.org/article/74f3c596fc2e47bdbf508c6047dddba7
Publikováno v:
IEEE Access, Vol 12, Pp 135135-135147 (2024)
Focusing on person re-identification datasets, this paper proposes a new method to estimate the test accuracy curve over the training image number in a precise, interpretable, and efficient manner to receive financial and privacy protection benefits.
Externí odkaz:
https://doaj.org/article/1cb779be046c4673a5893ac32e523e29
Autor:
Miho Takahashi, Kei Iino, Hiroshi Watanabe, Ichiro Morinaga, Shohei Enomoto, Xu Shi, Akira Sakamoto, Takeharu Eda
Publikováno v:
International Workshop on Advanced Imaging Technology (IWAIT) 2023.
Autor:
Kei Iino, Miho Takahashi, Hiroshi Watanabe, Ichiro Morinaga, Shohei Enomoto, Xu Shi, Akira Sakamoto, Takeharu Eda
Publikováno v:
2022 IEEE 11th Global Conference on Consumer Electronics (GCCE).
Publikováno v:
GCCE
The pervasiveness of “Internet-of-Things” in daily life has led to a recent surge in fog computing, encompassing a collaboration of cloud computing and edge intelligence. As a significant field of IoT, real-time detection and classification have
Publikováno v:
ICIP
Recent progress of Deep Learning has accelerated the spread of intelligent video analytics for surveillance cameras, which made us aware that the cost of GPUs is prohibitively high for analyzing huge video streams in production systems. As one soluti