  In this work, the case of semantic segmentation on a small image dataset
(simulated by 1000 randomly selected images from PASCAL VOC 2012), where only
weak supervision signals (scribbles from user interaction) are available is
studied. Especially, to tackle the problem of limited data annotations in image
segmentation, transferring different pre-trained models and CRF based methods
are applied to enhance the segmentation performance. To this end, RotNet,
DeeperCluster, and Semi&Weakly Supervised Learning (SWSL) pre-trained models
are transferred and finetuned in a DeepLab-v2 baseline, and dense CRF is
applied both as a post-processing and loss regularization technique. The
results of my study show that, on this small dataset, using a pre-trained
ResNet50 SWSL model gives results that are 7.4% better than applying an
ImageNet pre-trained model; moreover, for the case of training on the full
PASCAL VOC 2012 training data, this pre-training approach increases the mIoU
results by almost 4%. On the other hand, dense CRF is shown to be very
effective as well, enhancing the results both as a loss regularization
technique in weakly supervised training and as a post-processing tool.
