150 citations · 187 across the 24 of their papers we have counts for
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Machine Learning under Imperfect Data: Challenges and Methods
Masoumeh Zareapoor
Machine-learning models are commonly developed under an assumption that training and test data are sufficiently complete, balanced, labelled, and drawn from compatible distribution…
Task Switching Without Forgetting via Proximal Decoupling
Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger +2
In continual learning, the primary challenge is to learn new information without forgetting old knowledge. A common solution addresses this trade-off through regularization, penali…
Multi-Domain Learning with Global Expert Mapping
Pourya Shamsolmoali, Masoumeh Zareapoor, Huiyu Zhou +3
Human perception generalizes well across different domains, but most vision models struggle beyond their training data. This gap motivates multi-dataset learning, where a single mo…
HMR-Net: Hierarchical Modular Routing for Cross-Domain Object Detection in Aerial Images
Pourya Shamsolmoali, Masoumeh Zareapoor, Michael Felsberg +3
Despite advances in object detection, aerial imagery remains a challenging domain, as models often fail to generalize across variations in spatial resolution, scene composition, an…
IntRec: Intent-based Retrieval with Contrastive Refinement
Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger +1
Retrieving user-specified objects from complex scenes remains a challenging task, especially when queries are ambiguous or involve multiple similar objects. Existing open-vocabular…
Finding Structure in Continual Learning
Pourya Shamsolmoali, Masoumeh Zareapoor
Learning from a stream of tasks usually pits plasticity against stability: acquiring new knowledge often causes catastrophic forgetting of past information. Most methods address th…