Transcription of PANet: Few-Shot Image Semantic Segmentation With …
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PANet: Few-Shot Image Semantic Segmentation With …
PANet: Few-Shot Image Semantic Segmentation with Prototype AlignmentKaixin Wang1 Jun Hao Liew2 Yingtian Zou2 Daquan Zhou1 Jiashi Feng21 NGS, National University of Singapore2 ECE Department, National University of Singapore{ , the great progress made by deep CNNs in imagesemantic Segmentation , they typically require a large num-ber of densely-annotated images for training and are diffi-cult to generalize to unseen object categories. Few-Shot seg-mentation has thus been developed to learn to perform seg-mentation from only a few annotated examples. In this pa-per, we tackle the challenging Few-Shot Segmentation prob-lem from a metric learning perspective and present PANet,a novel prototype alignment network to better utilize theinformation of the support set. Our PANet learns class-specific prototype representations from a few support im-ages within an embedding space and then performs segmen-tation over the query images through matching each pixel tothe learned prototypes.}
in Figure 1. PANet first embeds different foreground ob-jects and background into different prototypes via a shared feature extractor. In this way, each learned prototype is rep-resentative for the corresponding class and meanwhile is sufficiently distinguishable from …
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