A New Fewshot Segmentation Network Based on Class Representation This paper studies fewshot segmentation, which is a task of predicting foreground mask of unseen classes by a few of annotations only, aided by a set of rich annotations already existed. The existing methods mainly focus the task on how to transfer segmentation cues from support images (labeled images) to query images (unlabeled images), and try to learn efficient and general transfer module that can be easily extended to unseen classes. However, it is proved to be a challenging task to learn the transfer module that is general to various classes. This paper solves fewshot segmentation in a new perspective of how to represent unseen classes by existing classes, and formulates fewshot segmentation as the representation process that represents unseen classes (in terms of forming the foreground prior) by existing classes precisely. Based on such idea, we propose a new class representation based fewshot segmentation framework, which firstly generates class activation map of unseen class based on the knowledge of existing classes, and then uses the map as foreground probability map to extract the foregrounds from query image. A new twobranch based fewshot segmentation network is proposed. Moreover, a new CAM generation module that extracts the CAM of unseen classes rather than the classical training classes is raised. We validate the effectiveness of our method on Pascal VOC 2012 dataset, the value FBIoU of oneshot and fiveshot arrives at 69.2% and 70.1% respectively, which outperforms the stateoftheart method.

Recognizing novel patterns via adversarial learning for oneshot semantic segmentation Abstract   Oneshot semantic segmentation aims to recognize unseen object regions by using the reference of only one annotated example. Many deep convolutional neural networks have achieved enormous success on this task. However, most of the existing methods only use a fixed annotated dataset to train the network. The remaining unannotated examples remain difficult to be leveraged and recognized. In this study, we propose a procedure based on the generative adversarial network to enable the oneshot semantic segmentation model for learning information from previously unknown categories. Our method contains a segmentation network that generates segmentation predictions. We then use a discriminator to differentiate the probability maps of segmentation prediction from the ground truth distribution. Consequently, we can ignore the pixels classified as fake and only use trustworthy regions as the label to train the segmentation network, thus achieving semisupervised learning. Experimental results demonstrate the effectiveness of the proposed adversarial learning method with an average gain of 49.7% accuracy score on the PASCAL VOC 2012 dataset.