15 citations · 16 across the 7 of their papers we have counts for
8 papers
ASD-FEAT: A Multi-Modal Infant Video-Derived Dataset for Early ASD Risk Prediction
Sidrah Liaqat, Halil Helvaci, Sen-Ching Cheung +4
Accurate early screening for Autism Spectrum Disorder (ASD) is a precursor to timely intervention, which is critical for improving cognitive and behavioral outcomes. We present ASD…
Boundary-Centric Clip-Budgeted Active Learning for Temporal Action Segmentation
Halil Ismail Helvaci, Sen-ching Samson Cheung
Temporal action segmentation (TAS) in untrimmed videos requires dense temporal supervision. However, most of the annotation cost is spent identifying action transitions where segme…
Learning Class Difficulty via Dynamic Focal Attention for Histopathology Segmentation
Lakmali Nadeesha Kumari Rathukohe Mudiyanselage, Sen-Ching Samson Cheung
Frequency-based loss reweighting, the standard remedy for imbalanced histopathology segmentation, implicitly assumes that rare classes are difficult. Yet difficulty also arises fro…
MMTA: Multi Membership Temporal Attention for Fine-Grained Stroke Rehabilitation Assessment
Halil Ismail Helvaci, Justin Huber, Jihye Bae +1
To empower the iterative assessments involved during a person's rehabilitation, automated assessment of a person's abilities during daily activities requires temporally precise seg…
Empowering Source-Free Domain Adaptation via MLLM-Guided Reliability-Based Curriculum Learning
Dongjie Chen, Kartik Patwari, Zhengfeng Lai +3
Existing SFDA methods struggle to fully use pre-trained knowledge and often rely on a single model's predictions or handcrafted prompts, limiting robustness under domain shift. Mul…
Localizing Moments of Actions in Untrimmed Videos of Infants with Autism Spectrum Disorder
Halil Ismail Helvaci, Sen-ching Samson Cheung, Chen-Nee Chuah +1
Autism Spectrum Disorder (ASD) presents significant challenges in early diagnosis and intervention, impacting children and their families. With prevalence rates rising, there is a…
DPGOMI: Differentially Private Data Publishing with Gaussian Optimized Model Inversion
Dongjie Chen, Sen-ching S. Cheung, Chen-Nee Chuah
High-dimensional data are widely used in the era of deep learning with numerous applications. However, certain data which has sensitive information are not allowed to be shared wit…
Differentially Private Generative Adversarial Networks with Model Inversion
Dongjie Chen, Sen-ching Samson Cheung, Chen-Nee Chuah +1
To protect sensitive data in training a Generative Adversarial Network (GAN), the standard approach is to use differentially private (DP) stochastic gradient descent method in whic…