Zobrazeno 1 - 10
of 576
pro vyhledávání: '"Liu Weijia"'
Publikováno v:
Современные инновации, системы и технологии, Vol 3, Iss 2 (2023)
"Crowded pedestrian detection" is a hot topic in the field of pedestrian detection. To address the issue of missed targets and small pedestrians in crowded scenes, an improved DETR object detection algorithm called DETR-crowd is proposed. The attenti
Externí odkaz:
https://doaj.org/article/afac1c8ccb03469fb5b55e340589736a
Research on computer vision application in industry field: focus on distribution network engineering
Publikováno v:
Современные инновации, системы и технологии, Vol 3, Iss 1 (2023)
The operation of distribution networks is currently facing potential safety and quality defects that pose significant hazards. One solution to strengthen management, reduce manual workload, and improve efficiency and quality is by applying deep detec
Externí odkaz:
https://doaj.org/article/d318724b88504877b2ab7b5b9fcf0d52
Given the complexity of power systems, particularly the high-dimensional variability of net loads, accurately depicting the entire operational range of net loads poses a challenge. To address this, recent methodologies have sought to gauge the maximu
Externí odkaz:
http://arxiv.org/abs/2407.14947
Autor:
HUANG Shaohong, LIU Weijia
Publikováno v:
口腔疾病防治, Vol 27, Iss 1, Pp 2-8 (2019)
As a professional method to prevent decayed teeth and an oral public health project, pit and fissure seal⁃ ant is widely used domestically. How to evaluate the effect and benefit of the community pit and fissure closure project, especially the co
Externí odkaz:
https://doaj.org/article/542abebcaef44ee083b5b0f28cf0ce87
Current methods for Video Moment Retrieval (VMR) struggle to align complex situations involving specific environmental details, character descriptions, and action narratives. To tackle this issue, we propose a Large Language Model-guided Moment Retri
Externí odkaz:
http://arxiv.org/abs/2405.12540
This paper studies the distributed least-squares optimization problem with differential privacy requirement of local cost functions, for which two differentially private distributed solvers are proposed. The first is established on the distributed gr
Externí odkaz:
http://arxiv.org/abs/2403.01435
Identifying labels that did not appear during training, known as multi-label zero-shot learning, is a non-trivial task in computer vision. To this end, recent studies have attempted to explore the multi-modal knowledge of vision-language pre-training
Externí odkaz:
http://arxiv.org/abs/2401.01181
Autor:
Zhu, Xuelin, Cao, Jiuxin, liu, Jian, Tang, Dongqi, Xu, Furong, Liu, Weijia, Ge, Jiawei, Liu, Bo, Guo, Qingpei, Zhang, Tianyi
Pre-trained vision-language models have notably accelerated progress of open-world concept recognition. Their impressive zero-shot ability has recently been transferred to multi-label image classification via prompt tuning, enabling to discover novel
Externí odkaz:
http://arxiv.org/abs/2312.04160
This paper studies the average consensus problem with differential privacy of initial states, for which it is widely recognized that there is a trade-off between the mean-square computation accuracy and privacy level. Considering the trade-off gap be
Externí odkaz:
http://arxiv.org/abs/2309.08464
Autor:
Zhang, Xiangyu, Eseye, Abinet Tesfaye, Knueven, Bernard, Liu, Weijia, Reynolds, Matthew, Jones, Wesley
This paper focuses on the critical load restoration problem in distribution systems following major outages. To provide fast online response and optimal sequential decision-making support, a reinforcement learning (RL) based approach is proposed to o
Externí odkaz:
http://arxiv.org/abs/2203.04166