Misc,
Semi and Weakly Supervised Semantic Segmentation Using Generative Adversarial Network
(2017)cite arxiv:1703.09695.
Abstract
Semantic segmentation has been a long standing challenging task in computer
vision. It aims at assigning a label to each image pixel and needs significant
number of pixellevel annotated data, which is often unavailable. To address
this lack, in this paper, we leverage, on one hand, massive amount of available
unlabeled or weakly labeled data, and on the other hand, non-real images
created through Generative Adversarial Networks. In particular, we propose a
semi-supervised framework ,based on Generative Adversarial Networks (GANs),
which consists of a generator network to provide extra training examples to a
multi-class classifier, acting as discriminator in the GAN framework, that
assigns sample a label y from the K possible classes or marks it as a fake
sample (extra class). The underlying idea is that adding large fake visual data
forces real samples to be close in the feature space, enabling a bottom-up
clustering process, which, in turn, improves multiclass pixel classification.
To ensure higher quality of generated images for GANs with consequent improved
pixel classification, we extend the above framework by adding weakly annotated
data, i.e., we provide class level information to the generator. We tested our
approaches on several challenging benchmarking visual datasets, i.e. PASCAL,
SiftFLow, Stanford and CamVid, achieving competitive performance also compared
to state-of-the-art semantic segmentation method
