activity
20182022
most citedA Real-Time Fusion Framework for Long-term Visual Localization

2 citations · 9 across the 7 of their papers we have counts for

collaborators

8 papers

cs.LG20221 cited

HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk Prediction

Youru Li, Zhenfeng Zhu, Xiaobo Guo +3

Risk prediction, as a typical time series modeling problem, is usually achieved by learning trends in markers or historical behavior from sequence data, and has been widely applied…

cs.CV20221 cited

Addressing Heterogeneity in Federated Learning via Distributional Transformation

Haolin Yuan, Bo Hui, Yuchen Yang +3

Federated learning (FL) allows multiple clients to collaboratively train a deep learning model. One major challenge of FL is when data distribution is heterogeneous, i.e., differs…

cs.CV20222 cited

A Real-Time Fusion Framework for Long-term Visual Localization

Yuchen Yang, Xudong Zhang, Shuang Gao +5

Visual localization is a fundamental task that regresses the 6 Degree Of Freedom (6DoF) poses with image features in order to serve the high precision localization requests in many…

cs.CV20211 cited

Retrieval and Localization with Observation Constraints

Yuhao Zhou, Huanhuan Fan, Shuang Gao +4

Accurate visual re-localization is very critical to many artificial intelligence applications, such as augmented reality, virtual reality, robotics and autonomous driving. To accom…

cs.CV20211 cited

Dual-Modality Vehicle Anomaly Detection via Bilateral Trajectory Tracing

Jingyuan Chen, Guanchen Ding, Yuchen Yang +7

Traffic anomaly detection has played a crucial role in Intelligent Transportation System (ITS). The main challenges of this task lie in the highly diversified anomaly scenes and va…

math.NA20201 cited

Adaptive regularisation for ensemble Kalman inversion

Marco Iglesias, Yuchen Yang

We propose a new regularisation strategy for the classical ensemble Kalman inversion (EKI) framework. The strategy consists of: (i) an adaptive choice for the regularisation parame…