1 code implementation • 25 May 2024 • Mang Ye, Wei Shen, Bo Du, Eduard Snezhko, Vassili Kovalev, Pong C. Yuen
Vertical Federated Learning (VFL) is a privacy-preserving distributed learning paradigm where different parties collaboratively learn models using partitioned features of shared samples, without leaking private data.
1 code implementation • 27 Apr 2024 • Haifeng Yang, Chuanxing Geng, Pong C. Yuen, Songcan Chen
In particular, a novel self-matching module is designed for OSSL, which can achieve the adaptation in automatically identifying known class samples while rejecting unknown class samples which are further utilized to enhance the discriminability of the model as the instantiated representation of unknown classes.
no code implementations • CVPR 2024 • CHONG YIN, SiQi Liu, Fei Lyu, Jiahao Lu, Sune Darkner, Vincent Wai-Sun Wong, Pong C. Yuen
Fibrosis staging from liver biopsy images plays a key role in demonstrating the histological progression of NAFLD.
1 code implementation • CVPR 2024 • CHONG YIN, SiQi Liu, Kaiyang Zhou, Vincent Wai-Sun Wong, Pong C. Yuen
QAP is based on two quantitative attributes namely K-function-based spatial attributes and histogram-based morphological attributes which are aimed for quantitative assessment of tissue states.
2 code implementations • 20 Jul 2023 • Mang Ye, Xiuwen Fang, Bo Du, Pong C. Yuen, DaCheng Tao
Therefore, a systematic survey on this topic about the research challenges and state-of-the-art is essential.
no code implementations • CVPR 2023 • Jingda Du, Si-Qi Liu, Bochao Zhang, Pong C. Yuen
Under different cross-domain scenarios, the comprehensive results show the effectiveness of our method.
1 code implementation • 12 Feb 2022 • Rui Shao, Pramuditha Perera, Pong C. Yuen, Vishal M. Patel
This paper proposes an Open-Set Defense Network with Clean-Adversarial Mutual Learning (OSDN-CAML) as a solution to the OSAD problem.
no code implementations • 25 Oct 2021 • Rui Shao, Bochao Zhang, Pong C. Yuen, Vishal M. Patel
The generalization ability of face presentation attack detection models to unseen attacks has become a key issue for real-world deployment, which can be improved when models are trained with face images from different input distributions and different types of spoof attacks.
no code implementations • 14 Apr 2021 • Rui Shao, Pramuditha Perera, Pong C. Yuen, Vishal M. Patel
A face presentation attack detection model with good generalization can be obtained when it is trained with face images from different input distributions and different types of spoof attacks.
1 code implementation • ECCV 2020 • Rui Shao, Pramuditha Perera, Pong C. Yuen, Vishal M. Patel
In this paper, we show that open-set recognition systems are vulnerable to adversarial attacks.
1 code implementation • 5 Aug 2020 • Jinpeng Wang, Yiqi Lin, Andy J. Ma, Pong C. Yuen
Without labelled data for network pretraining, temporal triplet is generated for each anchor video by using segment of the same or different time interval so as to enhance the capacity for temporal feature representation.
no code implementations • 29 May 2020 • Rui Shao, Pramuditha Perera, Pong C. Yuen, Vishal M. Patel
A face presentation attack detection model with good generalization can be obtained when it is trained with face images from different input distributions and different types of spoof attacks.
1 code implementation • 25 Nov 2019 • Rui Shao, Xiangyuan Lan, Pong C. Yuen
Besides, to further enhance the generalization ability of our model, the proposed framework adopts a fine-grained learning strategy that simultaneously conducts meta-learning in a variety of domain shift scenarios in each iteration.
1 code implementation • CVPR 2019 • Mang Ye, Xu Zhang, Pong C. Yuen, Shih-Fu Chang
This paper studies the unsupervised embedding learning problem, which requires an effective similarity measurement between samples in low-dimensional embedding space.
no code implementations • ECCV 2018 • Si-Qi Liu, Xiangyuan Lan, Pong C. Yuen
3D mask face presentation attack, as a new challenge in face recognition, has been attracting increasing attention.
no code implementations • ECCV 2018 • Mang Ye, Xiangyuan Lan, Pong C. Yuen
After that, a robust and efficient top-k counts label prediction strategy is proposed to predict the labels of unlabeled image sequences.
Ranked #11 on Person Re-Identification on PRID2011
Representation Learning Video-Based Person Re-Identification
no code implementations • 2 Mar 2017 • Guangcan Mai, Kai Cao, Pong C. Yuen, Anil K. Jain
Our study demonstrates the need to secure deep templates in face recognition systems.
no code implementations • 31 Oct 2016 • Frodo Kin Sun Chan, Andy J. Ma, Pong C. Yuen, Terry Cheuk-Fung Yip, Yee-Kit Tse, Vincent Wai-Sun Wong, Grace Lai-Hung Wong
Regular medical records are useful for medical practitioners to analyze and monitor patient health status especially for those with chronic disease, but such records are usually incomplete due to unpunctuality and absence of patients.
no code implementations • 18 Dec 2015 • Andy J. Ma, Pong C. Yuen, Suchi Saria
For robustness to significant pose variations, deformable spatial relationship between detectors are learnt in our multi-person tracking system.
no code implementations • CVPR 2014 • Xiangyuan Lan, Andy J. Ma, Pong C. Yuen
The use of multiple features for tracking has been proved as an effective approach because limitation of each feature could be compensated.