activity
20152026
most citedSemi-supervised few-shot learning for medical image segmentation

61 citations · 97 across the 33 of their papers we have counts for

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Showing 2021Show all

5 papers · 1 filter

cs.CV2021

F-CAM: Full Resolution Class Activation Maps via Guided Parametric Upscaling

Soufiane Belharbi, Aydin Sarraf, Marco Pedersoli +3

Class Activation Mapping (CAM) methods have recently gained much attention for weakly-supervised object localization (WSOL) tasks. They allow for CNN visualization and interpretati…

math.OC2021

A Preconditioned Alternating Minimization Framework for Nonconvex and Half Quadratic Optimization

Shengxiang Deng, Ismail Ben Ayed, Hongpeng Sun

For some typical and widely used non-convex half-quadratic regularization models and the Ambrosio-Tortorelli approximate Mumford-Shah model, based on the Kurdyka-Łojasiewicz analys…

cs.CV2021

Mutual-Information Based Few-Shot Classification

Malik Boudiaf, Ziko Imtiaz Masud, Jérôme Rony +3

We introduce Transductive Infomation Maximization (TIM) for few-shot learning. Our method maximizes the mutual information between the query features and their label predictions fo…

cs.LG20212 cited

Transductive Few-Shot Learning: Clustering is All You Need?

Imtiaz Masud Ziko, Malik Boudiaf, Jose Dolz +2

We investigate a general formulation for clustering and transductive few-shot learning, which integrates prototype-based objectives, Laplacian regularization and supervision constr…

cs.CV2021

Beyond pixel-wise supervision for segmentation: A few global shape descriptors might be surprisingly good!

Hoel Kervadec, Houda Bahig, Laurent Letourneau-Guillon +2

Standard losses for training deep segmentation networks could be seen as individual classifications of pixels, instead of supervising the global shape of the predicted segmentation…