Inter-class Discrepancy Alignment for Face Recognition

no code implementations • 2 Mar 2021 • Jiaheng Liu, Yudong Wu, Yichao Wu, Zhenmao Li, Chen Ken, Ding Liang, Junjie Yan

In this study, we make a key observation that the local con-text represented by the similarities between the instance and its inter-class neighbors1plays an important role forFR.

Face Recognition

DAM: Discrepancy Alignment Metric for Face Recognition

no code implementations • ICCV 2021 • Jiaheng Liu, Yudong Wu, Yichao Wu, Chuming Li, Xiaolin Hu, Ding Liang, Mengyu Wang

To estimate the LID of each face image in the verification process, we propose two types of LID Estimation (LIDE) methods, which are reference-based and learning-based estimation methods, respectively.

Face Recognition

Rotation Consistent Margin Loss for Efficient Low-Bit Face Recognition

no code implementations • CVPR 2020 • Yudong Wu, Yichao Wu, Ruihao Gong, Yuanhao Lv, Ken Chen, Ding Liang, Xiaolin Hu, Xianglong Liu, Junjie Yan

In this paper, we consider the low-bit quantization problem of face recognition (FR) under the open-set protocol.

Face Recognition Image Classification +1

Instance Scale Normalization for image understanding

no code implementations • 20 Aug 2019 • Zewen He, He Huang, Yudong Wu, Guan Huang, Wensheng Zhang

Scale variation remains a challenging problem for object detection.

Instance Segmentation Object +5

Learning to Auto Weight: Entirely Data-driven and Highly Efficient Weighting Framework

no code implementations • 27 May 2019 • Zhenmao Li, Yichao Wu, Ken Chen, Yudong Wu, Shunfeng Zhou, Jiaheng Liu, Junjie Yan

Example weighting algorithm is an effective solution to the training bias problem, however, most previous typical methods are usually limited to human knowledge and require laborious tuning of hyperparameters.