Fully Convolutional Multi-Class Multiple Instance Learning [article]

Deepak Pathak, Evan Shelhamer, Jonathan Long, Trevor Darrell
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
more » ... t. 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.
arXiv:1412.7144v4 fatcat:2jyzciesorem5ncvnbo6bkjtiu