Zobrazeno 1 - 10
of 97
pro vyhledávání: '"Jiang, Renhe"'
Human mobility prediction plays a critical role in applications such as disaster response, urban planning, and epidemic forecasting. Traditional methods often rely on designing crafted, domain-specific models, and typically focus on short-term predic
Externí odkaz:
http://arxiv.org/abs/2410.23692
Autor:
Zheng, Lele, Cao, Yang, Jiang, Renhe, Taura, Kenjiro, Shen, Yulong, Li, Sheng, Yoshikawa, Masatoshi
Recent works show that sensitive user data can be reconstructed from gradient updates, breaking the key privacy promise of federated learning. While success was demonstrated primarily on image data, these methods do not directly transfer to other dom
Externí odkaz:
http://arxiv.org/abs/2410.16121
With the popularity of location-based services, human mobility prediction plays a key role in enhancing personalized navigation, optimizing recommendation systems, and facilitating urban mobility and planning. This involves predicting a user's next P
Externí odkaz:
http://arxiv.org/abs/2410.14970
Free-space trajectory similarity calculation, e.g., DTW, Hausdorff, and Frechet, often incur quadratic time complexity, thus learning-based methods have been proposed to accelerate the computation. The core idea is to train an encoder to transform tr
Externí odkaz:
http://arxiv.org/abs/2410.14629
Scaling laws offer valuable insights into the design of time series foundation models (TSFMs). However, previous research has largely focused on the scaling laws of TSFMs for in-distribution (ID) data, leaving their out-of-distribution (OOD) scaling
Externí odkaz:
http://arxiv.org/abs/2410.12360
Cloth-changing person re-identification (CC-ReID) poses a significant challenge in computer vision. A prevailing approach is to prompt models to concentrate on causal attributes, like facial features and hairstyles, rather than confounding elements s
Externí odkaz:
http://arxiv.org/abs/2410.05536
Spatiotemporal neural networks have shown great promise in urban scenarios by effectively capturing temporal and spatial correlations. However, urban environments are constantly evolving, and current model evaluations are often limited to traffic sce
Externí odkaz:
http://arxiv.org/abs/2410.04740
In recent years, 3D vision has become a crucial field within computer vision, powering a wide range of applications such as autonomous driving, robotics, augmented reality (AR), and medical imaging. This field relies on the accurate perception, under
Externí odkaz:
http://arxiv.org/abs/2410.04738
Traffic forecasting is a cornerstone of smart city management, enabling efficient resource allocation and transportation planning. Deep learning, with its ability to capture complex nonlinear patterns in spatiotemporal (ST) data, has emerged as a pow
Externí odkaz:
http://arxiv.org/abs/2410.00385
Recent advancements in Spatiotemporal Graph Neural Networks (ST-GNNs) and Transformers have demonstrated promising potential for traffic forecasting by effectively capturing both temporal and spatial correlations. The generalization ability of spatio
Externí odkaz:
http://arxiv.org/abs/2410.00373