activity
20222024
most citedFedDisco: Federated Learning with Discrepancy-Aware Collaboration

25 citations · 33 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CV2024

Self-Supervised Bird's Eye View Motion Prediction with Cross-Modality Signals

Shaoheng Fang, Zuhong Liu, Mingyu Wang +3

Learning the dense bird's eye view (BEV) motion flow in a self-supervised manner is an emerging research for robotics and autonomous driving. Current self-supervised methods mainly…

cs.LG2023

Compatible Transformer for Irregularly Sampled Multivariate Time Series

Yuxi Wei, Juntong Peng, Tong He +4

To analyze multivariate time series, most previous methods assume regular subsampling of time series, where the interval between adjacent measurements and the number of samples rem…

cs.CV20232 cited

Auxiliary Tasks Benefit 3D Skeleton-based Human Motion Prediction

Chenxin Xu, Robby T. Tan, Yuhong Tan +3

Exploring spatial-temporal dependencies from observed motions is one of the core challenges of human motion prediction. Previous methods mainly focus on dedicated network structure…

cs.CV20231 cited

Joint-Relation Transformer for Multi-Person Motion Prediction

Qingyao Xu, Weibo Mao, Jingze Gong +5

Multi-person motion prediction is a challenging problem due to the dependency of motion on both individual past movements and interactions with other people. Transformer-based meth…

cs.LG202325 cited

FedDisco: Federated Learning with Discrepancy-Aware Collaboration

Rui Ye, Mingkai Xu, Jianyu Wang +3

This work considers the category distribution heterogeneity in federated learning. This issue is due to biased labeling preferences at multiple clients and is a typical setting of…

cs.CV20233 cited

EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning

Chenxin Xu, Robby T. Tan, Yuhong Tan +4

Learning to predict agent motions with relationship reasoning is important for many applications. In motion prediction tasks, maintaining motion equivariance under Euclidean geomet…