1 citations · 1 across the 3 of their papers we have counts for
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LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning
Bharadwaj Ravichandran, Alexander Lynch, Sarah Brockman +4
Both few-shot learning and domain adaptation sub-fields in Computer Vision have seen significant recent progress in terms of the availability of state-of-the-art algorithms and dat…
GeoWATCH for Detecting Heavy Construction in Heterogeneous Time Series of Satellite Images
Jon Crall, Connor Greenwell, David Joy +3
Learning from multiple sensors is challenging due to spatio-temporal misalignment and differences in resolution and captured spectra. To that end, we introduce GeoWATCH, a flexible…
Open Set Action Recognition via Multi-Label Evidential Learning
Chen Zhao, Dawei Du, Anthony Hoogs +1
Existing methods for open-set action recognition focus on novelty detection that assumes video clips show a single action, which is unrealistic in the real world. We propose a new…
Discover and Mitigate Unknown Biases with Debiasing Alternate Networks
Zhiheng Li, Anthony Hoogs, Chenliang Xu
Deep image classifiers have been found to learn biases from datasets. To mitigate the biases, most previous methods require labels of protected attributes (e.g., age, skin tone) as…