7 citations · 13 across the 34 of their papers we have counts for
15 papers · 1 filter
LT-Soups: Bridging Head and Tail Classes via Subsampled Model Soups
Masih Aminbeidokhti, Subhankar Roy, Eric Granger +2
Real-world datasets typically exhibit long-tailed (LT) distributions, where a few head classes dominate and many tail classes are severely underrepresented. While recent work shows…
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…
Robust and Efficient Finetuning of Vision Foundation Models via Implicit Ensembling
Masih Aminbeidokhti, Heitor Rapela Medeiros, Eric Granger +1
Robust finetuning of foundation models seeks to improve out-of-distribution (OOD) generalization during in-distribution (ID) adaptation. We revisit Mixout, a stochastic regularizer…
VLOD-TTA: Test-Time Adaptation of Vision-Language Object Detectors
Atif Belal, Heitor R. Medeiros, Marco Pedersoli +1
Vision-language object detectors (VLODs) such as YOLO-World and Grounding DINO exhibit strong zero-shot generalization, but their performance degrades under distribution shift. Tes…
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…
Personalized Feature Translation for Expression Recognition: An Efficient Source-Free Domain Adaptation Method
Masoumeh Sharafi, Soufiane Belharbi, Muhammad Osama Zeeshan +6
Facial expression recognition (FER) models are widely used in video-based affective computing applications, such as human-computer interaction and healthcare monitoring. However, d…
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…
Infrared Object Detection with Ultra Small ConvNets: Is ImageNet Pretraining Still Useful?
Srikanth Muralidharan, Heitor R. Medeiros, Masih Aminbeidokhti +2
Many real-world applications require recognition models that are robust to different operational conditions and modalities, but at the same time run on small embedded devices, with…
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…
BAH Dataset for Ambivalence/Hesitancy Recognition in Videos for Digital Behavioural Change
Manuela González-González, Soufiane Belharbi, Muhammad Osama Zeeshan +6
Ambivalence and hesitancy (A/H), closely related constructs, are the primary reasons why individuals delay, avoid, or abandon health behaviour changes. They are subtle and conflict…
Learning from Stochastic Teacher Representations Using Student-Guided Knowledge Distillation
Muhammad Haseeb Aslam, Clara Martinez, Marco Pedersoli +3
Advances in self-distillation have shown that when knowledge is distilled from a teacher to a student using the same deep learning (DL) architecture, the student performance can su…
Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation
Nairouz Mrabah, Nicolas Richet, Ismail Ben Ayed +1
Adapting Vision-Language Models (VLMs) to new domains with few labeled samples remains a significant challenge due to severe overfitting and computational constraints. State-of-the…
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…
Disentangled Source-Free Personalization for Facial Expression Recognition with Neutral Target Data
Masoumeh Sharafi, Emma Ollivier, Muhammad Osama Zeeshan +5
Facial Expression Recognition (FER) from videos is a crucial task in various application areas, such as human-computer interaction and health diagnosis and monitoring (e.g., assess…
TeD-Loc: Text Distillation for Weakly Supervised Object Localization
Shakeeb Murtaza, Soufiane Belharbi, Alexis Guichemerre +2
Weakly supervised object localization (WSOL) models can predict both the object class and the spatial regions corresponding to the object, without requiring explicit bounding-box a…