Instance segmentation is the problem of detecting and delineating each distinct object of interest appearing in an image. Current instance segmentation approaches consist of ensembles of modules that are trained independently of each other, thus missing learning opportunities. Here we propose a new instance segmentation paradigm consisting in an end-to-end method that learns how to segment instances sequentially. The model is based on a recurrent neural network that sequentially finds objects and their segmentations one at a time. This net is provided with a spatial memory that keeps track of what pixels have been explained and allows handling occlusion. In order to train the model we designed a new principled loss function that accurately represents the properties of the instance segmentation problem. In the experiments carried out, we found that our method outperforms recent approaches on multiple person segmentation, and all state of the art approaches on the Plant Phenotyping dataset for leaf counting.
Parallel to our work, REF proposes a recurrent neural net to label object instances sequential by keeping a memory of which pixels have been labeled so far.
Recurrent Instance Segmentation
ECCV 2016. 14th European Conference on Computer Vision
Computer Science