activity
20182021
most citedLearning Modulated Loss for Rotated Object Detection

63 citations · 77 across the 8 of their papers we have counts for

collaborators

9 papers

cs.CV20211 cited

Label-Occurrence-Balanced Mixup for Long-tailed Recognition

Shaoyu Zhang, Chen Chen, Xiujuan Zhang +1

Mixup is a popular data augmentation method, with many variants subsequently proposed. These methods mainly create new examples via convex combination of random data pairs and thei…

cs.CV20211 cited

RSDet++: Point-based Modulated Loss for More Accurate Rotated Object Detection

Wen Qian, Xue Yang, Silong Peng +2

We classify the discontinuity of loss in both five-param and eight-param rotated object detection methods as rotation sensitivity error (RSE) which will result in performance degen…

cs.CV2021

Towards Fine-grained 3D Face Dense Registration: An Optimal Dividing and Diffusing Method

Zhenfeng Fan, Silong Peng, Shihong Xia

Dense vertex-to-vertex correspondence between 3D faces is a fundamental and challenging issue for 3D&2D face analysis. While the sparse landmarks have anatomically ground-truth cor…

cs.CV202110 cited

Balanced Knowledge Distillation for Long-tailed Learning

Shaoyu Zhang, Chen Chen, Xiyuan Hu +1

Deep models trained on long-tailed datasets exhibit unsatisfactory performance on tail classes. Existing methods usually modify the classification loss to increase the learning foc…

cs.CV2020

Progressive Bilateral-Context Driven Model for Post-Processing Person Re-Identification

Min Cao, Chen Chen, Hao Dou +3

Most existing person re-identification methods compute pairwise similarity by extracting robust visual features and learning the discriminative metric. Owing to visual ambiguities,…

cs.CV20202 cited

PCA-SRGAN: Incremental Orthogonal Projection Discrimination for Face Super-resolution

Hao Dou, Chen Chen, Xiyuan Hu +3

Generative Adversarial Networks (GAN) have been employed for face super resolution but they bring distorted facial details easily and still have weakness on recovering realistic te…