paper-conference

Applicability of Facial Video-Based Alertness Estimation Model in Real Online and In-Person Classrooms
Accurately capturing learners’ internal states is essential in modern educational environments to support effective teaching and the …
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Auxiliary Gene Learning: Spatial Gene Expression Estimation by Auxiliary Gene Selection
Spatial transcriptomics (ST) is a novel technology that enables the observation of gene expression at the resolution of individual …
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Discriminative Analysis of Autistic Tendencies at 18 Months of Age Using Eye Gaze Characteristics in 4-, 10-, and 18-month-old Infants
Anomalies in motor function and social communication skills constitute early signs of autism spectrum disorder (ASD). In recent years, …
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Gaussian-Based Instance-Adaptive Intensity Modeling for Point-Supervised Facial Expression Spotting
Point-supervised facial expression spotting (P-FES) aims to localize facial expression instances in untrimmed videos, requiring only a …
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Impact of Experimental Design in Age Prediction from Retinal Fundus Images
Artificial intelligence (AI) provides promising insights to improve and support medical diagnostics. However, the design of AI systems …
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Learning Relative Gene Expression Trends from Pathology Images in Spatial Transcriptomics
Gene expression estimation from pathology images has the potential to reduce the RNA sequencing cost. Point-wise loss functions have …
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Multi-Task Learning of Classification and Generation for Set-Structured Data
In this study we propose a multi-task learning model of classification and generation for set-structured data. The proposed model …
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Non-Negative Tensor Factorization of Infant Spontaneous Movements: A Pilot Study for ASD Risk Evaluation of Newborn Infants
Early detection of infants with autism spectrum disorder (ASD) can lead to effective developmental support. In clinical practice, early …
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CALICO: Confident Active Learning with Integrated Calibration
The growing use of deep learning in safety-critical applications, such as medical imaging, has raised concerns about limited labeled …
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Instruct Me More! Random Prompting for Visual In-Context Learning
Large-scale models trained on extensive datasets, have emerged as the preferred approach due to their high generalizability across …
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