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Found 3 papers, 2 papers with code
United We Learn Better: Harvesting Learning Improvements From Class Hierarchies Across Tasks
Attempts of learning from hierarchical taxonomies in computer vision have been mostly focusing on image classification.
Prior to Segment: Foreground Cues for Weakly Annotated Classes in Partially Supervised Instance Segmentation
Instance segmentation methods require large datasets with expensive and thus limited instance-level mask labels.
Dynamic Adaptation on Non-Stationary Visual Domains
To demonstrate the effectiveness of our proposed framework, we modify associative domain adaptation to work well on source and target data batches with unequal class distributions.
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