7 citations · 13 across the 34 of their papers we have counts for
6 papers · 1 filter
Pain Analysis using Adaptive Hierarchical Spatiotemporal Dynamic Imaging
Issam Serraoui, Eric Granger, Abdenour Hadid +1
Automatic pain intensity estimation plays a pivotal role in healthcare and medical fields. While many methods have been developed to gauge human pain using behavioral or physiologi…
Subject-Based Domain Adaptation for Facial Expression Recognition
Muhammad Osama Zeeshan, Muhammad Haseeb Aslam, Soufiane Belharbi +4
Adapting a deep learning model to a specific target individual is a challenging facial expression recognition (FER) task that may be achieved using unsupervised domain adaptation (…
Evaluating Supervision Levels Trade-Offs for Infrared-Based People Counting
David Latortue, Moetez Kdayem, Fidel A Guerrero Peña +2
Object detection models are commonly used for people counting (and localization) in many applications but require a dataset with costly bounding box annotations for training. Given…
DiPS: Discriminative Pseudo-Label Sampling with Self-Supervised Transformers for Weakly Supervised Object Localization
Shakeeb Murtaza, Soufiane Belharbi, Marco Pedersoli +2
Self-supervised vision transformers (SSTs) have shown great potential to yield rich localization maps that highlight different objects in an image. However, these maps remain class…
Domain Generalization by Rejecting Extreme Augmentations
Masih Aminbeidokhti, Fidel A. Guerrero Peña, Heitor Rapela Medeiros +3
Data augmentation is one of the most effective techniques for regularizing deep learning models and improving their recognition performance in a variety of tasks and domains. Howev…
HalluciDet: Hallucinating RGB Modality for Person Detection Through Privileged Information
Heitor Rapela Medeiros, Fidel A. Guerrero Pena, Masih Aminbeidokhti +3
A powerful way to adapt a visual recognition model to a new domain is through image translation. However, common image translation approaches only focus on generating data from the…