170 citations · 468 across the 48 of their papers we have counts for
8 papers · 1 filter
Distilling Vision-Language Pre-training to Collaborate with Weakly-Supervised Temporal Action Localization
Chen Ju, Kunhao Zheng, Jinxiang Liu +5
Weakly-supervised temporal action localization (WTAL) learns to detect and classify action instances with only category labels. Most methods widely adopt the off-the-shelf Classifi…
FedSkip: Combatting Statistical Heterogeneity with Federated Skip Aggregation
Ziqing Fan, Yanfeng Wang, Jiangchao Yao +3
The statistical heterogeneity of the non-independent and identically distributed (non-IID) data in local clients significantly limits the performance of federated learning. Previou…
A Simple Plugin for Transforming Images to Arbitrary Scales
Qinye Zhou, Ziyi Li, Weidi Xie +3
Existing models on super-resolution often specialized for one scale, fundamentally limiting their use in practical scenarios. In this paper, we aim to develop a general plugin that…
Skeleton-Parted Graph Scattering Networks for 3D Human Motion Prediction
Maosen Li, Siheng Chen, Zijing Zhang +3
Graph convolutional network based methods that model the body-joints' relations, have recently shown great promise in 3D skeleton-based human motion prediction. However, these meth…
Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting
Bohan Tang, Yiqi Zhong, Chenxin Xu +5
In multi-modal multi-agent trajectory forecasting, two major challenges have not been fully tackled: 1) how to measure the uncertainty brought by the interaction module that causes…
Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational Reasoning
Chenxin Xu, Yuxi Wei, Bohan Tang +3
Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction. However, previous works mainly c…
Contrastive Learning with Boosted Memorization
Zhihan Zhou, Jiangchao Yao, Yanfeng Wang +2
Self-supervised learning has achieved a great success in the representation learning of visual and textual data. However, the current methods are mainly validated on the well-curat…
GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoning
Chenxin Xu, Maosen Li, Zhenyang Ni +2
Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction. However, previous works only con…