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Fully Convolutional Multi-Class Multiple Instance Learning
[article]
2015
arXiv
pre-print
Multiple instance learning (MIL) can reduce the need for costly annotation in tasks such as semantic segmentation by weakening the required degree of supervision. We propose a novel MIL formulation of multi-class semantic segmentation learning by a fully convolutional network. In this setting, we seek to learn a semantic segmentation model from just weak image-level labels. The model is trained end-to-end to jointly optimize the representation while disambiguating the pixel-image label
arXiv:1412.7144v4
fatcat:2jyzciesorem5ncvnbo6bkjtiu