Zobrazeno 1 - 10
of 339
pro vyhledávání: '"Gao Yingjie"'
Publikováno v:
E3S Web of Conferences, Vol 512, p 02008 (2024)
Ensuring the safety of tunnel structures in unique underwater environments is of paramount importance. This study focuses on the entrance section of a river-crossing tunnel, characterized by shallow burial depths with significant variations. The tunn
Externí odkaz:
https://doaj.org/article/7f325a22072c4b64a53eb55a75375da2
Quantum information entropy is regarded as a measure of coherence between the observed system and the environment or between many-body. It is commonly described as the uncertainty and purity of a mixed state of a quantum system. Different from tradit
Externí odkaz:
http://arxiv.org/abs/2411.09150
In recent years, Few-Shot Object Detection (FSOD) has gained widespread attention and made significant progress due to its ability to build models with a good generalization power using extremely limited annotated data. The fine-tuning based paradigm
Externí odkaz:
http://arxiv.org/abs/2408.05674
In computer vision, object detection is an important task that finds its application in many scenarios. However, obtaining extensive labels can be challenging, especially in crowded scenes. Recently, the Segment Anything Model (SAM) has been proposed
Externí odkaz:
http://arxiv.org/abs/2407.11464
Few-shot object detection~(FSOD), which aims to detect novel objects with limited annotated instances, has made significant progress in recent years. However, existing methods still suffer from biased representations, especially for novel classes in
Externí odkaz:
http://arxiv.org/abs/2406.13498
Autor:
Feng, Yongchao, Li, Shiwei, Gao, Yingjie, Huang, Ziyue, Zhang, Yanan, Liu, Qingjie, Wang, Yunhong
Though feature-alignment based Domain Adaptive Object Detection (DAOD) methods have achieved remarkable progress, they ignore the source bias issue, i.e., the detector tends to acquire more source-specific knowledge, impeding its generalization capab
Externí odkaz:
http://arxiv.org/abs/2311.10437
Annotating remote sensing images (RSIs) presents a notable challenge due to its labor-intensive nature. Semi-supervised object detection (SSOD) methods tackle this issue by generating pseudo-labels for the unlabeled data, assuming that all classes fo
Externí odkaz:
http://arxiv.org/abs/2310.05498
Autor:
Chai, Jiaxin, Gu, Xiangyang, Song, Pengyu, Zhao, Xinzhou, Gao, Yingjie, Wang, Haiqi, Zhang, Qian, Cai, Tingting, Liu, Yutong, Li, Xiaoting, Song, Tao, Zhu, Zhengge
Publikováno v:
In Plant Physiology and Biochemistry December 2024 217
Autor:
Dong, Zhi Liang, Yuan, Yi, Martins, Vinicius, Jin, Enzhong, Gan, Yi, Lin, Xiaoting, Gao, Yingjie, Hao, Xiaoge, Guan, Yi, Fu, Jiamin, Pang, Xin, Huang, Yining, Tu, Qingsong Howard, Sham, Tsun-Kong, Zhao, Yang
Publikováno v:
In Nano Energy September 2024 128 Part A
Publikováno v:
In International Journal of Applied Earth Observation and Geoinformation May 2024 129