Zobrazeno 1 - 10
of 570
pro vyhledávání: '"Yang Wenxian"'
Autor:
Lin, Yuxiang, Luo, Ling, Chen, Ying, Zhang, Xushi, Wang, Zihui, Yang, Wenxian, Tong, Mengsha, Yu, Rongshan
Spatial transcriptomics (ST) provides high-resolution pathological images and whole-transcriptomic expression profiles at individual spots across whole-slide scales. This setting makes it an ideal data source to develop multimodal foundation models.
Externí odkaz:
http://arxiv.org/abs/2411.16793
Temporal perception, the ability to detect and track objects over time, is critical in autonomous driving for maintaining a comprehensive understanding of dynamic environments. However, this task is hindered by significant challenges, including incom
Externí odkaz:
http://arxiv.org/abs/2411.14927
Infrastructure sensors installed at elevated positions offer a broader perception range and encounter fewer occlusions. Integrating both infrastructure and ego-vehicle data through V2X communication, known as vehicle-infrastructure cooperation, has s
Externí odkaz:
http://arxiv.org/abs/2408.10531
Multi-modal learning that combines pathological images with genomic data has significantly enhanced the accuracy of survival prediction. Nevertheless, existing methods have not fully utilized the inherent hierarchical structure within both whole slid
Externí odkaz:
http://arxiv.org/abs/2404.08027
Cooperatively utilizing both ego-vehicle and infrastructure sensor data via V2X communication has emerged as a promising approach for advanced autonomous driving. However, current research mainly focuses on improving individual modules, rather than t
Externí odkaz:
http://arxiv.org/abs/2404.00717
Autor:
Hao, Ruiyang, Fan, Siqi, Dai, Yingru, Zhang, Zhenlin, Li, Chenxi, Wang, Yuntian, Yu, Haibao, Yang, Wenxian, Yuan, Jirui, Nie, Zaiqing
The value of roadside perception, which could extend the boundaries of autonomous driving and traffic management, has gradually become more prominent and acknowledged in recent years. However, existing roadside perception approaches only focus on the
Externí odkaz:
http://arxiv.org/abs/2403.10145
Autor:
Li, Hao, Chen, Ying, Chen, Yifei, Yang, Wenxian, Ding, Bowen, Han, Yuchen, Wang, Liansheng, Yu, Rongshan
Whole Slide Image (WSI) classification is often formulated as a Multiple Instance Learning (MIL) problem. Recently, Vision-Language Models (VLMs) have demonstrated remarkable performance in WSI classification. However, existing methods leverage coars
Externí odkaz:
http://arxiv.org/abs/2402.19326
Motion forecasting is an essential task for autonomous driving, and utilizing information from infrastructure and other vehicles can enhance forecasting capabilities. Existing research mainly focuses on leveraging single-frame cooperative information
Externí odkaz:
http://arxiv.org/abs/2311.00371
Cooperative perception can effectively enhance individual perception performance by providing additional viewpoint and expanding the sensing field. Existing cooperation paradigms are either interpretable (result cooperation) or flexible (feature coop
Externí odkaz:
http://arxiv.org/abs/2308.01804
Autor:
Yu, Haibao, Yang, Wenxian, Ruan, Hongzhi, Yang, Zhenwei, Tang, Yingjuan, Gao, Xu, Hao, Xin, Shi, Yifeng, Pan, Yifeng, Sun, Ning, Song, Juan, Yuan, Jirui, Luo, Ping, Nie, Zaiqing
Utilizing infrastructure and vehicle-side information to track and forecast the behaviors of surrounding traffic participants can significantly improve decision-making and safety in autonomous driving. However, the lack of real-world sequential datas
Externí odkaz:
http://arxiv.org/abs/2305.05938