Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?

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 meta-learning paradigm. We introduce a transductive inference for a given query image, leveraging the statistics of its unl...

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Veröffentlicht in:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) S. 13974 - 13983
Hauptverfasser:	Boudiaf, Malik, Kervadec, Hoel, Masud, Ziko Imtiaz, Piantanida, Pablo, Ayed, Ismail Ben, Dolz, Jose
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.06.2021
Schlagworte:	Benchmark testing Codes Computer vision Entropy Feature extraction Image segmentation Training
ISSN:	1063-6919
Online-Zugang:	Volltext
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