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Carlo Biffi

2 accepted papers

2020

Many-shot from Low-shot: Learning to Annotate using Mixed Supervision for Object Detection

ECCV 2020poster

Object detection has witnessed significant progress by relying on large, manually annotated datasets. Annotating such datasets is highly time consuming and expensive, which motivates the development of weakly supervised and few-shot object detection methods. However, these methods largely underperfo…

Cited by 18SourcePDFScholar
2020

Self-supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation

ECCV 2020poster

Few-shot semantic segmentation (FSS) has great potential for medical imaging applications. Most of the existing FSS techniques require abundant annotated semantic classes for training. However, these methods may not be applicable for medical images due to the lack of annotations. To address this pro…