3 citations · 5 across the 11 of their papers we have counts for
11 papers · 1 filter
UMCL: Unimodal-generated Multimodal Contrastive Learning for Cross-compression-rate Deepfake Detection
Ching-Yi Lai, Chih-Yu Jian, Pei-Cheng Chuang +4
In deepfake detection, the varying degrees of compression employed by social media platforms pose significant challenges for model generalization and reliability. Although existing…
Robust Multi-Modal Face Anti-Spoofing with Domain Adaptation: Tackling Missing Modalities, Noisy Pseudo-Labels, and Model Degradation
Ming-Tsung Hsu, Fang-Yu Hsu, Yi-Ting Lin +6
Recent multi-modal face anti-spoofing (FAS) methods have investigated the potential of leveraging multiple modalities to distinguish live and spoof faces. However, pre-adapted mult…
Multi-Modal Face Anti-Spoofing via Cross-Modal Feature Transitions
Jun-Xiong Chong, Fang-Yu Hsu, Ming-Tsung Hsu +4
Multi-modal face anti-spoofing (FAS) aims to detect genuine human presence by extracting discriminative liveness cues from multiple modalities, such as RGB, infrared (IR), and dept…
Ultra-Short rPPG Estimation via Periodicity Guidance and Signal Reconstruction
Pei-Kai Huang, Ya-Ting Chan, Kuan-Wen Chen +3
Many remote Heart Rate (HR) measurement methods focus on estimating remote photoplethysmography (rPPG) signals from video clips lasting around 10 seconds but often overlook the nee…
Enhancing Learnable Descriptive Convolutional Vision Transformer for Face Anti-Spoofing
Pei-Kai Huanga, Jun-Xiong Chong, Ming-Tsung Hsu +2
Face anti-spoofing (FAS) heavily relies on identifying live/spoof discriminative features to counter face presentation attacks. Recently, we proposed LDCformer to successfully inco…
SLIP: Spoof-Aware One-Class Face Anti-Spoofing with Language Image Pretraining
Pei-Kai Huang, Jun-Xiong Chong, Cheng-Hsuan Chiang +3
Face anti-spoofing (FAS) plays a pivotal role in ensuring the security and reliability of face recognition systems. With advancements in vision-language pretrained (VLP) models, re…
Self-supervised Feature Disentanglement and Augmentation Network for One-class Face Anti-spoofing
Pei-Kai Huang, Jun-Xiong Chong, Ming-Tsung Hsu +5
Face anti-spoofing (FAS) techniques aim to enhance the security of facial identity authentication by distinguishing authentic live faces from deceptive attempts. While two-class FA…
Fully Test-Time rPPG Estimation via Synthetic Signal-Guided Feature Learning
Pei-Kai Huang, Tzu-Hsien Chen, Ya-Ting Chan +2
Many remote photoplethysmography (rPPG) estimation models have achieved promising performance in the training domain but often fail to accurately estimate physiological signals or…
DD-rPPGNet: De-interfering and Descriptive Feature Learning for Unsupervised rPPG Estimation
Pei-Kai Huang, Tzu-Hsien Chen, Ya-Ting Chan +2
Remote Photoplethysmography (rPPG) aims to measure physiological signals and Heart Rate (HR) from facial videos. Recent unsupervised rPPG estimation methods have shown promising po…
Generating Self-Guided Dense Annotations for Weakly Supervised Semantic Segmentation
Zi-Yi Ke, Chiou-Ting Hsu
Learning semantic segmentation models under image-level supervision is far more challenging than under fully supervised setting. Without knowing the exact pixel-label correspondenc…
Bayesian image segmentations by Potts prior and loopy belief propagation
Kazuyuki Tanaka, Shun Kataoka, Muneki Yasuda +2
This paper presents a Bayesian image segmentation model based on Potts prior and loopy belief propagation. The proposed Bayesian model involves several terms, including the pairwis…