activity
20172022
most citedJointly Attentive Spatial-Temporal Pooling Networks for Video-based Person Re-Identification

40 citations · 65 across the 9 of their papers we have counts for

collaborators

10 papers

cs.CV20216 cited

DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation

Maosheng Ye, Shuangjie Xu, Tongyi Cao +1

We present a novel and flexible architecture for point cloud segmentation with dual-representation iterative learning. In point cloud processing, different representations have the…

cs.CV20213 cited

Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network

Zhikang Zou, Xiaoye Qu, Pan Zhou +4

Recent deep networks have convincingly demonstrated high capability in crowd counting, which is a critical task attracting widespread attention due to its various industrial applic…

cs.CV20202 cited

Spatiotemporal Graph Neural Network based Mask Reconstruction for Video Object Segmentation

Daizong Liu, Shuangjie Xu, Xiao-Yang Liu +3

This paper addresses the task of segmenting class-agnostic objects in semi-supervised setting. Although previous detection based methods achieve relatively good performance, these…

cs.CV2020

HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection

Maosheng Ye, Shuangjie Xu, Tongyi Cao

We present Hybrid Voxel Network (HVNet), a novel one-stage unified network for point cloud based 3D object detection for autonomous driving. Recent studies show that 2D voxelizatio…

cs.CV2020

Crowd Counting via Hierarchical Scale Recalibration Network

Zhikang Zou, Yifan Liu, Shuangjie Xu +3

The task of crowd counting is extremely challenging due to complicated difficulties, especially the huge variation in vision scale. Previous works tend to adopt a naive concatenati…

cs.CV20201 cited

Dynamic Graph Correlation Learning for Disease Diagnosis with Incomplete Labels

Daizong Liu, Shuangjie Xu, Pan Zhou +3

Disease diagnosis on chest X-ray images is a challenging multi-label classification task. Previous works generally classify the diseases independently on the input image without co…