6 papers
Rethinking Object-Centric Representations for Video Dynamics Modeling
Amaury Wei, Ismail Nejjar, Olga Fink
Unsupervised video object tracking aims to decompose dynamic scenes into persistent, object-centric entities without manual annotations. Many recent approaches rely on slot-based r…
From Physics to Machine Learning and Back: Part II - Learning and Observational Bias in PHM
Olga Fink, Ismail Nejjar, Vinay Sharma +13
Prognostics and Health Management ensures the reliability, safety, and efficiency of complex engineered systems by enabling fault detection, anticipating equipment failures, and op…
Efficient Unsupervised Domain Adaptation Regression for Spatial-Temporal Sensor Fusion
Keivan Faghih Niresi, Ismail Nejjar, Olga Fink
The growing deployment of low-cost, distributed sensor networks in environmental and biomedical domains has enabled continuous, large-scale health monitoring. However, these system…
Recall and Refine: A Simple but Effective Source-free Open-set Domain Adaptation Framework
Ismail Nejjar, Hao Dong, Olga Fink
Open-set Domain Adaptation (OSDA) aims to adapt a model from a labeled source domain to an unlabeled target domain, where novel classes - also referred to as target-private unknown…
DynAlign: Unsupervised Dynamic Taxonomy Alignment for Cross-Domain Segmentation
Han Sun, Rui Gong, Ismail Nejjar +1
Current unsupervised domain adaptation (UDA) methods for semantic segmentation typically assume identical class labels between the source and target domains. This assumption ignore…
Uncertainty-Guided Alignment for Unsupervised Domain Adaptation in Regression
Ismail Nejjar, Gaetan Frusque, Florent Forest +1
Unsupervised Domain Adaptation for Regression (UDAR) aims to adapt models from a labeled source domain to an unlabeled target domain for regression tasks. Traditional feature align…