Zobrazeno 1 - 10
of 729
pro vyhledávání: '"ZHANG Jiaqing"'
Publikováno v:
Dizhi lixue xuebao, Vol 30, Iss 2, Pp 260-274 (2024)
Objective The northern margin fault zone of the Qaidam Basin is a regional active fault zone in the northern part of the Qinghai-Tibet Plateau, forming the boundary fault of the northern Qaidam Basin and the Qilian Mountains. Studying its late Quater
Externí odkaz:
https://doaj.org/article/e7b35983badb4c9bad9a3a52c9558bc4
Publikováno v:
IEEE Access, Vol 11, Pp 133246-133254 (2023)
Fire detection is an important technology to reliably guarantee the safety of power transmission scenarios. An effective fire detection model can help fire-fighting robots be deployed in a timely and accurate manner. However, under hazy conditions, t
Externí odkaz:
https://doaj.org/article/3d34e956b3a346549bef6a0e6e647d92
Autor:
Bandyopadhyay, Sabyasachi, Zhang, Jiaqing, Ison, Ronald L., Libon, David J., Tighe, Patrick, Price, Catherine, Rashidi, Parisa
The association between preoperative cognitive status and surgical outcomes is a critical, yet scarcely explored area of research. Linking intraoperative data with postoperative outcomes is a promising and low-cost way of evaluating long-term impacts
Externí odkaz:
http://arxiv.org/abs/2411.00840
Autor:
Contreras, Miguel, Kapoor, Sumit, Zhang, Jiaqing, Davidson, Andrea, Ren, Yuanfang, Guan, Ziyuan, Ozrazgat-Baslanti, Tezcan, Nerella, Subhash, Bihorac, Azra, Rashidi, Parisa
Delirium is an acute confusional state that has been shown to affect up to 31% of patients in the intensive care unit (ICU). Early detection of this condition could lead to more timely interventions and improved health outcomes. While artificial inte
Externí odkaz:
http://arxiv.org/abs/2410.17363
Autor:
Dong, Shuhan, Li, Yunsong, Xie, Weiying, Zhang, Jiaqing, Tian, Jiayuan, Yang, Danian, Lei, Jie
Multimodal object detection leverages diverse modal information to enhance the accuracy and robustness of detectors. By learning long-term dependencies, Transformer can effectively integrate multimodal features in the feature extraction stage, which
Externí odkaz:
http://arxiv.org/abs/2410.11358
Autor:
Li, Daixun, Xie, Weiying, Cao, Mingxiang, Wang, Yunke, Zhang, Jiaqing, Li, Yunsong, Fang, Leyuan, Xu, Chang
Multimodal image fusion and segmentation enhance scene understanding in autonomous driving by integrating data from various sensors. However, current models struggle to efficiently segment densely packed elements in such scenes, due to the absence of
Externí odkaz:
http://arxiv.org/abs/2408.13980
Multimodal object detection offers a promising prospect to facilitate robust detection in various visual conditions. However, existing two-stream backbone networks are challenged by complex fusion and substantial parameter increments. This is primari
Externí odkaz:
http://arxiv.org/abs/2407.16129
Autor:
Shen, Tingjia, Wang, Hao, Zhang, Jiaqing, Zhao, Sirui, Li, Liangyue, Chen, Zulong, Lian, Defu, Chen, Enhong
Cross-Domain Sequential Recommendation (CDSR) aims to mine and transfer users' sequential preferences across different domains to alleviate the long-standing cold-start issue. Traditional CDSR models capture collaborative information through user and
Externí odkaz:
http://arxiv.org/abs/2406.03085
The majority of existing hyperspectral anomaly detection (HAD) methods use the low-rank representation (LRR) model to separate the background and anomaly components, where the anomaly component is optimized by handcrafted sparse priors (e.g., $\ell_{
Externí odkaz:
http://arxiv.org/abs/2404.13342
Autor:
Zhang, Luankang, Wang, Hao, Zhang, Suojuan, Yin, Mingjia, Han, Yongqiang, Zhang, Jiaqing, Lian, Defu, Chen, Enhong
Cross-domain recommendation (CDR), aiming to extract and transfer knowledge across domains, has attracted wide attention for its efficacy in addressing data sparsity and cold-start problems. Despite significant advances in representation disentanglem
Externí odkaz:
http://arxiv.org/abs/2404.00268