Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Yujie Huang"'
Autor:
Junying Zeng, Boyuan Zhu, Yujie Huang, Chuanbo Qin, Jingming Zhu, Fan Wang, Yikui Zhai, Junying Gan, Yucong Chen, Yingbo Wang, Ruggero Donida Labati, Vincenzo Piuri, Fabio Scotti
Publikováno v:
IEEE Access, Vol 9, Pp 303-316 (2021)
Because the existing finger vein segmentation networks are too large and not suitable for implementation in mobile terminals, the reduction of the parameters of the lightweight network leads to the reduction of the segmentation index, and the long-ru
Externí odkaz:
https://doaj.org/article/b8563d7000c54ec2b0f8dd0cd6f3fa21
Publikováno v:
2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA).
Publikováno v:
2022 IEEE 16th International Conference on Solid-State & Integrated Circuit Technology (ICSICT).
Publikováno v:
2021 IEEE 14th International Conference on ASIC (ASICON).
Publikováno v:
2021 IEEE 14th International Conference on ASIC (ASICON).
Publikováno v:
2021 IEEE/CVF International Conference on Computer Vision (ICCV).
Publikováno v:
ISCAS
Since deep learning was introduced into style transfer, remarkable results have been achieved in it and it is widely used in multimedia fields, such as photography. However, the computational costs of the existing state-of-the-art (SOTA) arbitrary st
Publikováno v:
ISCAS
Finding enough accurate matching points is key for image stitching. However, the existing state-of-the-art algorithms fail to find enough accurate matching points when facing the challenge where detectable features are not obvious. In this paper, a n
Publikováno v:
2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications( AEECA).
In view of the simple structure of a single neural network model, the traditional convolutional neural network cannot fully extract deep text features. This paper proposes a text sentiment classification based on Attention Mechanism and Decomposition
Publikováno v:
2019 IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA).
Style transfer is a research hotspot in computer vision. Up to now, it is still a challenge although many researches have been conducted on it for high quality style transfer. In this work, we propose an algorithm named ASTCNN which is a real-time Ar