no code implementations • 1 Mar 2024 • Qiang Meng, Xiao Wang, Jiabao Wang, Liujiang Yan, Ke Wang
Our proposed Small, Versatile, and Mighty (SVM) network utilizes a pure convolutional architecture to fully unleash the efficiency and multi-tasking potentials of the range view representation.
1 code implementation • CVPR 2023 • Ziyue Zhu, Qiang Meng, Xiao Wang, Ke Wang, Liujiang Yan, Jian Yang
For the loss design, we propose the COMLoss to dynamically predict object-level difficulties and emphasize objects of different difficulties based on training stages.
1 code implementation • 30 Jul 2022 • Qiang Meng, Feng Zhou
Given limited public projects in this field, codes of our method and implemented baselines are made open-source in https://github. com/IrvingMeng/SecureVector.
no code implementations • ICLR 2022 • Qiang Meng, Feng Zhou, Hainan Ren, Tianshu Feng, Guochao Liu, Yuanqing Lin
The growing public concerns on data privacy in face recognition can be greatly addressed by the federated learning (FL) paradigm.
no code implementations • 23 Jan 2022 • Qiang Meng, Xinqian Gu, Xiaqing Xu, Feng Zhou
Experimentally, we demonstrate the efficiency and superiority of the BBS on the tasks of face recognition and re-identification, with both simulated and real-world datasets.
1 code implementation • ICCV 2021 • Qiang Meng, Chixiang Zhang, Xiaoqiang Xu, Feng Zhou
Achieving backward compatibility when rolling out new models can highly reduce costs or even bypass feature re-encoding of existing gallery images for in-production visual retrieval systems.
no code implementations • 25 Jul 2021 • Qiang Meng, Xiaqing Xu, Xiaobo Wang, Yang Qian, Yunxiao Qin, Zezheng Wang, Chenxu Zhao, Feng Zhou, Zhen Lei
Despite the great success achieved by deep learning methods in face recognition, severe performance drops are observed for large pose variations in unconstrained environments (e. g., in cases of surveillance and photo-tagging).
2 code implementations • CVPR 2021 • Qiang Meng, Shichao Zhao, Zhida Huang, Feng Zhou
This paper proposes MagFace, a category of losses that learn a universal feature embedding whose magnitude can measure the quality of the given face.
Ranked #1 on Face Verification on IJB-C (training dataset metric)
no code implementations • 10 Feb 2021 • Xiaqing Xu, Qiang Meng, Yunxiao Qin, Jianzhu Guo, Chenxu Zhao, Feng Zhou, Zhen Lei
A standard pipeline of current face recognition frameworks consists of four individual steps: locating a face with a rough bounding box and several fiducial landmarks, aligning the face image using a pre-defined template, extracting representations and comparing.