2 citations · 2 across the 12 of their papers we have counts for
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WiSE-OD: Benchmarking Robustness in Infrared Object Detection
Heitor R. Medeiros, Atif Belal, Masih Aminbeidokhti +2
Object detection (OD) in infrared (IR) imagery is critical for low-light and nighttime applications. However, the scarcity of large-scale IR datasets forces models to rely on weigh…
Low-Rank Expert Merging for Multi-Source Domain Adaptation in Person Re-Identification
Taha Mustapha Nehdi, Nairouz Mrabah, Atif Belal +2
Adapting person re-identification (reID) models to new target environments remains a challenging problem that is typically addressed using unsupervised domain adaptation (UDA) meth…
MuSACo: Multimodal Subject-Specific Selection and Adaptation for Expression Recognition with Co-Training
Muhammad Osama Zeeshan, Natacha Gillet, Alessandro Lameiras Koerich +3
Personalized expression recognition (ER) involves adapting a machine learning model to subject-specific data for improved recognition of expressions with considerable interpersonal…
Progressive Multi-Source Domain Adaptation for Personalized Facial Expression Recognition
Muhammad Osama Zeeshan, Marco Pedersoli, Alessandro Lameiras Koerich +1
Personalized facial expression recognition (FER) involves adapting a machine learning model using samples from labeled sources and unlabeled target domains. Given the challenges of…
High-Rate Mixout: Revisiting Mixout for Robust Domain Generalization
Masih Aminbeidokhti, Heitor Rapela Medeiros, Srikanth Muralidharan +2
Ensembling fine-tuned models initialized from powerful pre-trained weights is a common strategy to improve robustness under distribution shifts, but it comes with substantial compu…
Revisiting Mixout: An Overlooked Path to Robust Finetuning
Masih Aminbeidokhti, Heitor Rapela Medeiros, Eric Granger +1
Finetuning vision foundation models often improves in-domain accuracy but comes at the cost of robustness under distribution shift. We revisit Mixout, a stochastic regularizer that…