activity
20172022
most citedGroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoning

7 citations · 15 across the 7 of their papers we have counts for

collaborators

16 papers

cs.CV2022

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…

cs.CV20227 cited

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…

cs.CV2018

Domain-Invariant Adversarial Learning for Unsupervised Domain Adaption

Yexun Zhang, Ya Zhang, Yanfeng Wang +1

Unsupervised domain adaption aims to learn a powerful classifier for the target domain given a labeled source data set and an unlabeled target data set. To alleviate the effect of…

cs.CV2018

Phase Collaborative Network for Two-Phase Medical Image Segmentation

Huangjie Zheng, Lingxi Xie, Tianwei Ni +5

In real-world practice, medical images acquired in different phases possess complementary information, {\em e.g.}, radiologists often refer to both arterial and venous scans in ord…

cs.LG2018

Variational Collaborative Learning for User Probabilistic Representation

Kenan Cui, Xu Chen, Jiangchao Yao +1

Collaborative filtering (CF) has been successfully employed by many modern recommender systems. Conventional CF-based methods use the user-item interaction data as the sole informa…

cs.CV2018

A Unified Framework for Generalizable Style Transfer: Style and Content Separation

Yexun Zhang, Ya Zhang, Wenbin Cai

Image style transfer has drawn broad attention in recent years. However, most existing methods aim to explicitly model the transformation between different styles, and the learned…