Search Results for author:
Found 16 papers, 6 papers with code
Heterogeneous Federated Learning: State-of-the-art and Research Challenges
Therefore, a systematic survey on this topic about the research challenges and state-of-the-art is essential.
Dual-Bridging With Adversarial Noise Generation for Domain Adaptive rPPG Estimation
Under different cross-domain scenarios, the comprehensive results show the effectiveness of our method.
Open-set Adversarial Defense with Clean-Adversarial Mutual Learning
This paper proposes an Open-Set Defense Network with Clean-Adversarial Mutual Learning (OSDN-CAML) as a solution to the OSAD problem.
Federated Test-Time Adaptive Face Presentation Attack Detection with Dual-Phase Privacy Preservation
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.
Federated Generalized Face Presentation Attack Detection
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.
Open-set Adversarial Defense
In this paper, we show that open-set recognition systems are vulnerable to adversarial attacks.
Self-supervised Temporal Discriminative Learning for Video Representation Learning
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.
Federated Face Presentation Attack Detection
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.
Regularized Fine-grained Meta Face Anti-spoofing
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.
Unsupervised Embedding Learning via Invariant and Spreading Instance Feature
This paper studies the unsupervised embedding learning problem, which requires an effective similarity measurement between samples in low-dimensional embedding space.
Robust Anchor Embedding for Unsupervised Video Person Re-Identification in the Wild
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
Remote Photoplethysmography Correspondence Feature for 3D Mask Face Presentation Attack Detection
3D mask face presentation attack, as a new challenge in face recognition, has been attracting increasing attention.
On the Reconstruction of Face Images from Deep Face Templates
Our study demonstrates the need to secure deep templates in face recognition systems.
Temporal Matrix Completion with Locally Linear Latent Factors for Medical Applications
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.
Deformable Distributed Multiple Detector Fusion for Multi-Person Tracking
For robustness to significant pose variations, deformable spatial relationship between detectors are learnt in our multi-person tracking system.
Multi-Cue Visual Tracking Using Robust Feature-Level Fusion Based on Joint Sparse Representation
The use of multiple features for tracking has been proved as an effective approach because limitation of each feature could be compensated.
