6 citations · 7 across the 5 of their papers we have counts for
6 papers
LLM-based Knowledge Pruning for Time Series Data Analytics on Edge-computing Devices
Ruibing Jin, Qing Xu, Min Wu +4
Limited by the scale and diversity of time series data, the neural networks trained on time series data often overfit and show unsatisfacotry performances. In comparison, large lan…
SEA++: Multi-Graph-based High-Order Sensor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation
Yucheng Wang, Yuecong Xu, Jianfei Yang +4
Unsupervised Domain Adaptation (UDA) methods have been successful in reducing label dependency by minimizing the domain discrepancy between a labeled source domain and an unlabeled…
MoPA: Multi-Modal Prior Aided Domain Adaptation for 3D Semantic Segmentation
Haozhi Cao, Yuecong Xu, Jianfei Yang +3
Multi-modal unsupervised domain adaptation (MM-UDA) for 3D semantic segmentation is a practical solution to embed semantic understanding in autonomous systems without expensive poi…
Multi-Modal Continual Test-Time Adaptation for 3D Semantic Segmentation
Haozhi Cao, Yuecong Xu, Jianfei Yang +3
Continual Test-Time Adaptation (CTTA) generalizes conventional Test-Time Adaptation (TTA) by assuming that the target domain is dynamic over time rather than stationary. In this pa…
Augmenting and Aligning Snippets for Few-Shot Video Domain Adaptation
Yuecong Xu, Jianfei Yang, Yunjiao Zhou +3
For video models to be transferred and applied seamlessly across video tasks in varied environments, Video Unsupervised Domain Adaptation (VUDA) has been introduced to improve the…
Leveraging Endo- and Exo-Temporal Regularization for Black-box Video Domain Adaptation
Yuecong Xu, Jianfei Yang, Haozhi Cao +4
To enable video models to be applied seamlessly across video tasks in different environments, various Video Unsupervised Domain Adaptation (VUDA) methods have been proposed to impr…