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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Vydané v:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 13974 - 13983
Hlavní autori:	Boudiaf, Malik, Kervadec, Hoel, Masud, Ziko Imtiaz, Piantanida, Pablo, Ayed, Ismail Ben, Dolz, Jose
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.06.2021
Predmet:	Benchmark testing Codes Computer vision Entropy Feature extraction Image segmentation Training
ISSN:	1063-6919
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