61 citations · 97 across the 20 of their papers we have counts for
27 papers · 1 filter
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…
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…
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…
Teach me to segment with mixed supervision: Confident students become masters
Jose Dolz, Christian Desrosiers, Ismail Ben Ayed
Deep segmentation neural networks require large training datasets with pixel-wise segmentations, which are expensive to obtain in practice. Mixed supervision could mitigate this di…
Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?
Malik Boudiaf, Hoel Kervadec, Ziko Imtiaz Masud +3
We show that the way inference is performed in few-shot segmentation tasks has a substantial effect on performances -- an aspect often overlooked in the literature in favor of the…
Deep Active Learning for Joint Classification & Segmentation with Weak Annotator
Soufiane Belharbi, Ismail Ben Ayed, Luke McCaffrey +1
CNN visualization and interpretation methods, like class-activation maps (CAMs), are typically used to highlight the image regions linked to class predictions. These models allow t…