8 papers · 1 filter
Rhythm-Structured Predictive Learning for Remote Photoplethysmography
Ba-Thinh Nguyen, Huu-Dung Nguyen, Thi-Duyen Ngo +1
Remote photoplethysmography (rPPG) estimates physiological signals from facial videos by analyzing subtle pulse induced skin color variations. Despite recent progress, existing sel…
VisAnalog: A Diagnostic Suite for Visual Concept Transfer on Natural Images
Zhaonan Li, Kyle R. Chickering, Bangzheng Li +13
A useful test of visual concept learning is not just whether a model can recognize a concept in a single image, but whether it can preserve and manipulate concept-level properties…
Efficient Bilevel Optimization for Meta Label Correction in Noisy Label Learning
Ba Hoang Anh Nguyen, Viet Cuong Ta
Training a deep neural network with noisy labels could reduce data annotation cost but may introduce noise into the learned model. In meta label correction approaches, an additiona…
BTS-rPPG: Orthogonal Butterfly Temporal Shifting for Remote Photoplethysmography
Ba-Thinh Nguyen, Thi-Duyen Ngo, Thanh-Trung Huynh +2
Remote photoplethysmography (rPPG) enables contactless physiological sensing from facial videos by analyzing subtle appearance variations induced by blood circulation. However, mod…
HOT: Harmonic-Constrained Optimal Transport for Remote Photoplethysmography Domain Adaptation
Ba-Thinh Nguyen, Thi-Duyen Ngo, Thanh-Trung Huynh +2
Remote photoplethysmography (rPPG) enables non-contact physiological measurement from facial videos; however, its practical deployment is often hindered by substantial performance…
A Unified View of Score-Based and Drifting Models
Chieh-Hsin Lai, Bac Nguyen, Naoki Murata +5
Drifting models train one-step generators by optimizing a kernel-induced mean-shift discrepancy between the data and model distributions, with Laplace kernels used by default in pr…
Improved Object-Centric Diffusion Learning with Registers and Contrastive Alignment
Bac Nguyen, Yuhta Takida, Naoki Murata +4
Slot Attention (SA) with pretrained diffusion models has recently shown promise for object-centric learning (OCL), but suffers from slot entanglement and weak alignment between obj…
GUDA: Counterfactual Group-wise Training Data Attribution for Diffusion Models via Unlearning
Naoki Murata, Yuhta Takida, Chieh-Hsin Lai +4
Training-data attribution for vision generative models aims to identify which training data influenced a given output. While most methods score individual examples, practitioners o…