  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.

Computer Vision and Pattern Recognition;Machine Learning;Neural and Evolutionary Computing