Zobrazeno 1 - 10
of 252
pro vyhledávání: '"Yang, Kaixiang"'
Autor:
Yang, Kaixiang, Shan, Wenqi, Li, Xudong, Wang, Xuan, Yang, Xikai, Wang, Xi, Heng, Pheng-Ann, Li, Qiang, Wang, Zhiwei
Multi-modal brain tumor segmentation typically involves four magnetic resonance imaging (MRI) modalities, while incomplete modalities significantly degrade performance. Existing solutions employ explicit or implicit modality adaptation, aligning feat
Externí odkaz:
http://arxiv.org/abs/2408.08708
Despite the prevalence of reconstruction-based deep learning methods, time series anomaly detection remains challenging. Existing approaches often struggle with limited temporal contexts, inadequate representation of normal patterns, and flawed evalu
Externí odkaz:
http://arxiv.org/abs/2405.11238
Optimization algorithms and large language models (LLMs) enhance decision-making in dynamic environments by integrating artificial intelligence with traditional techniques. LLMs, with extensive domain knowledge, facilitate intelligent modeling and st
Externí odkaz:
http://arxiv.org/abs/2405.10098
The aspiration of the next generation's autonomous driving (AD) technology relies on the dedicated integration and interaction among intelligent perception, prediction, planning, and low-level control. There has been a huge bottleneck regarding the u
Externí odkaz:
http://arxiv.org/abs/2401.12888
Anomaly detection in time series analysis is a pivotal task, yet it poses the challenge of discerning normal and abnormal patterns in label-deficient scenarios. While prior studies have largely employed reconstruction-based approaches, which limits t
Externí odkaz:
http://arxiv.org/abs/2401.09793
Autor:
Zhu, Chengyuan, Yang, Yiyuan, Yang, Kaixiang, Zhang, Haifeng, Yang, Qinmin, Chen, C. L. Philip
The application of artificial intelligence technology has greatly enhanced and fortified the safety of energy pipelines, particularly in safeguarding against external threats. The predominant methods involve the integration of intelligent sensors to
Externí odkaz:
http://arxiv.org/abs/2312.11583
Autor:
Luo, Xiangde, Fu, Jia, Zhong, Yunxin, Liu, Shuolin, Han, Bing, Astaraki, Mehdi, Bendazzoli, Simone, Toma-Dasu, Iuliana, Ye, Yiwen, Chen, Ziyang, Xia, Yong, Su, Yanzhou, Ye, Jin, He, Junjun, Xing, Zhaohu, Wang, Hongqiu, Zhu, Lei, Yang, Kaixiang, Fang, Xin, Wang, Zhiwei, Lee, Chan Woong, Park, Sang Joon, Chun, Jaehee, Ulrich, Constantin, Maier-Hein, Klaus H., Ndipenoch, Nchongmaje, Miron, Alina, Li, Yongmin, Zhang, Yimeng, Chen, Yu, Bai, Lu, Huang, Jinlong, An, Chengyang, Wang, Lisheng, Huang, Kaiwen, Gu, Yunqi, Zhou, Tao, Zhou, Mu, Zhang, Shichuan, Liao, Wenjun, Wang, Guotai, Zhang, Shaoting
Radiation therapy is a primary and effective NasoPharyngeal Carcinoma (NPC) treatment strategy. The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis. P
Externí odkaz:
http://arxiv.org/abs/2312.09576
Efficiently capturing the complex spatiotemporal representations from large-scale unlabeled traffic data remains to be a challenging task. In considering of the dilemma, this work employs the advanced contrastive learning and proposes a novel Spatial
Externí odkaz:
http://arxiv.org/abs/2307.02507
Publikováno v:
In Knowledge-Based Systems 27 September 2024 300
Publikováno v:
In Information Sciences August 2024 677