most citedLearning from Future: A Novel Self-Training Framework for Semantic Segmentation

21 citations · 47 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2022

D: Duplicate Detection Decontaminator for Multi-Athlete Tracking in Sports Videos

Rui He, Zehua Fu, Qingjie Liu +2

Tracking multiple athletes in sports videos is a very challenging Multi-Object Tracking (MOT) task, since athletes often have the same appearance and are intimately covered with ea…

cs.CV202221 cited

Learning from Future: A Novel Self-Training Framework for Semantic Segmentation

Ye Du, Yujun Shen, Haochen Wang +6

Self-training has shown great potential in semi-supervised learning. Its core idea is to use the model learned on labeled data to generate pseudo-labels for unlabeled samples, and…

cs.CV20226 cited

SparseTT: Visual Tracking with Sparse Transformers

Zhihong Fu, Zehua Fu, Qingjie Liu +2

Transformers have been successfully applied to the visual tracking task and significantly promote tracking performance. The self-attention mechanism designed to model long-range de…

cs.CV20212 cited

Weakly-Supervised Photo-realistic Texture Generation for 3D Face Reconstruction

Xiangnan Yin, Di Huang, Zehua Fu +2

Although much progress has been made recently in 3D face reconstruction, most previous work has been devoted to predicting accurate and fine-grained 3D shapes. In contrast, relativ…

cs.CV20211 cited

Pixel Sampling for Style Preserving Face Pose Editing

Xiangnan Yin, Di Huang, Hongyu Yang +3

The existing auto-encoder based face pose editing methods primarily focus on modeling the identity preserving ability during pose synthesis, but are less able to preserve the image…

cs.CV202117 cited

STMTrack: Template-free Visual Tracking with Space-time Memory Networks

Zhihong Fu, Qingjie Liu, Zehua Fu +1

Boosting performance of the offline trained siamese trackers is getting harder nowadays since the fixed information of the template cropped from the first frame has been almost tho…