Zobrazeno 1 - 10
of 838
pro vyhledávání: '"ZHANG Jiaqing"'
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
Autor:
Zhang, Jiaqing, Cao, Mingxiang, Yang, Xue, Xie, Weiying, Lei, Jie, Li, Daixun, Huang, Wenbo, Li, Yunsong
Multimodal image fusion and object detection are crucial for autonomous driving. While current methods have advanced the fusion of texture details and semantic information, their complex training processes hinder broader applications. Addressing this
Externí odkaz:
http://arxiv.org/abs/2403.09323
Autor:
Siegel, Scott, Zhang, Jiaqing, Bandyopadhyay, Sabyasachi, Nerella, Subhash, Silva, Brandon, Baslanti, Tezcan, Bihorac, Azra, Rashidi, Parisa
Despite the importance of closely monitoring patients in the Intensive Care Unit (ICU), many aspects are still assessed in a limited manner due to the time constraints imposed on healthcare providers. For example, although excessive visitations durin
Externí odkaz:
http://arxiv.org/abs/2403.06322