activity
20192023
most citedRGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion

12 citations · 25 across the 15 of their papers we have counts for

collaborators

20 papers

cs.CV20231 cited

SMC-UDA: Structure-Modal Constraint for Unsupervised Cross-Domain Renal Segmentation

Zhusi Zhong, Jie Li, Lulu Bi +6

Medical image segmentation based on deep learning often fails when deployed on images from a different domain. The domain adaptation methods aim to solve domain-shift challenges, b…

cs.LG2022

Multi-task Learning for Sparse Traffic Forecasting

Jiezhang Li, Junjun Li, Yue-Jiao Gong

Accurate traffic prediction is crucial to improve the performance of intelligent transportation systems. Previous traffic prediction tasks mainly focus on small and non-isolated tr…

cs.CV2022

Depth Is All You Need for Monocular 3D Detection

Dennis Park, Jie Li, Dian Chen +2

A key contributor to recent progress in 3D detection from single images is monocular depth estimation. Existing methods focus on how to leverage depth explicitly, by generating pse…

cs.LG20221 cited

Seen to Unseen: When Fuzzy Inference System Predicts IoT Device Positioning Labels That Had Not Appeared in Training Phase

Han Xu, Zheming Zuo, Jie Li +1

Situating at the core of Artificial Intelligence (AI), Machine Learning (ML), and more specifically, Deep Learning (DL) have embraced great success in the past two decades. However…

cs.LG2022

On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach

Jiawei Sun, Ruoxin Chen, Jie Li +3

Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (GRL) without the supervision of manual annotations. GCL can generate graph-level…

cs.CV20213 cited

DPointNet: A Density-Oriented PointNet for 3D Object Detection in Point Clouds

Jie Li, Yu Hu

For current object detectors, the scale of the receptive field of feature extraction operators usually increases layer by layer. Those operators are called scale-oriented operators…