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 assignment. Fully convolutional training accepts inputs of any size, does not need object proposal pre-processing, and offers a pixelwise loss map for selecting latent instances. Our multi-class MIL loss exploits the further supervision given by images with multiple labels. We evaluate this approach through preliminary experiments on the PASCAL VOC segmentation challenge.
@article{arxiv.1412.7144,
title = {Fully Convolutional Multi-Class Multiple Instance Learning},
author = {Deepak Pathak and Evan Shelhamer and Jonathan Long and Trevor Darrell},
journal= {arXiv preprint arXiv:1412.7144},
year = {2015}
}