%0 Generic %1 pathak2014fully %A Pathak, Deepak %A Shelhamer, Evan %A Long, Jonathan %A Darrell, Trevor %D 2014 %K image label weakly %T Fully Convolutional Multi-Class Multiple Instance Learning %U http://arxiv.org/abs/1412.7144 %X 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.

@misc{pathak2014fully, abstract = {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.}, added-at = {2018-09-19T18:40:09.000+0200}, author = {Pathak, Deepak and Shelhamer, Evan and Long, Jonathan and Darrell, Trevor}, biburl = {https://www.bibsonomy.org/bibtex/28fea75ab178e98d285c040220586af12/huntstart}, description = {[1412.7144] Fully Convolutional Multi-Class Multiple Instance Learning}, interhash = {cec2b271d78548b6c1149724dce852b5}, intrahash = {8fea75ab178e98d285c040220586af12}, keywords = {image label weakly}, note = {cite arxiv:1412.7144Comment: in ICLR 2015}, timestamp = {2018-09-19T19:56:44.000+0200}, title = {Fully Convolutional Multi-Class Multiple Instance Learning}, url = {http://arxiv.org/abs/1412.7144}, year = 2014 }