1 citations · 1 across the 8 of their papers we have counts for
9 papers
Generalist Multimodal LLMs Gain Biometric Expertise via Human Salience
Jacob Piland, Byron Dowling, Christopher Sweet +1
Iris presentation attack detection (PAD) is critical for secure biometric deployments, yet developing specialized models faces significant practical barriers: collecting data repre…
VISER: Visually-Informed System for Enhanced Robustness in Iris Presentation Attack Detection
Byron Dowling, Jacob Piland, Eleanor Frederick +2
Human perceptual priors have shown promise in saliency-guided deep learning training, particularly in the domain of iris presentation attack detection (PAD). Common saliency approa…
Divisive Decisions: Improving Salience-Based Training for Generalization in Binary Classification Tasks
Jacob Piland, Chris Sweet, Adam Czajka
Existing saliency-guided training approaches improve model generalization by incorporating a loss term that compares the model's class activation map (CAM) for a sample's true-clas…
DiffGradCAM: A Class Activation Map Using the Full Model Decision to Solve Unaddressed Adversarial Attacks
Jacob Piland, Chris Sweet, Adam Czajka
Class Activation Mapping (CAM) and its gradient-based variants (e.g., GradCAM) have become standard tools for explaining Convolutional Neural Network (CNN) predictions. However, th…
Non-Generative Energy Based Models
Jacob Piland, Christopher Sweet, Priscila Saboia +2
Energy-based models (EBM) have become increasingly popular within computer vision. EBMs bring a probabilistic approach to training deep neural networks (DNN) and have been shown to…
Explain To Me: Salience-Based Explainability for Synthetic Face Detection Models
Colton Crum, Patrick Tinsley, Aidan Boyd +5
The performance of convolutional neural networks has continued to improve over the last decade. At the same time, as model complexity grows, it becomes increasingly more difficult…
Improving Model's Focus Improves Performance of Deep Learning-Based Synthetic Face Detectors
Jacob Piland, Adam Czajka, Christopher Sweet
Deep learning-based models generalize better to unknown data samples after being guided "where to look" by incorporating human perception into training strategies. We made an obser…
Dynamic network analysis improves protein 3D structural classification
Khalique Newaz, Jacob Piland, Patricia L. Clark +3
Protein structural classification (PSC) is a supervised problem of assigning proteins into pre-defined structural (e.g., CATH or SCOPe) classes based on the proteins' sequence or 3…
Network analysis of synonymous codon usage
Khalique Newaz, Gabriel Wright, Jacob Piland +4
Most amino acids are encoded by multiple synonymous codons. For an amino acid, some of its synonymous codons are used much more rarely than others. Analyses of positions of such ra…