BriefGPT.xyz
Apr, 2018
学习看见不可见的:端到端可训练的平均实例分割
Learning to See the Invisible: End-to-End Trainable Amodal Instance Segmentation
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Patrick Follmann, Rebecca König, Philipp Härtinger, Michael Klostermann
TL;DR
本文提出了第一个全新的端到端可训练模型,为语义无模态分割预测非实例的遮挡区域,实验结果表明,本模型选择的架构有助于无模态分割,并在COCO无模态数据及两个新数据集上均提供了强大的基础性能。
Abstract
semantic amodal segmentation
is a recently proposed extension to
instance-aware segmentation
that includes the prediction of the
invisible region
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