Few-Shot Segmentation for Prohibited Items Inspection With Patch-Based Self-Supervised Learning and Prototype Reverse Validation

Prohibited items inspection using X-ray screening is essential for reducing the risk of crime and terrorist attacks. The difficulty in prohibited items inspection lies in accurately detecting prohibited items in complex X-ray images and limited access to X-ray images containing prohibited items. Few-shot segmentation aims at learning with limited examples and assigning a category label to each image pixel. However, current few-shot methods are mostly full-supervised and less robust to the prohibited items categories that did not appear during training process. In this paper, we propose a method for few-shot prohibited items segmentation tasks which utilize unlabeled data and better leverage the representation of input samples during model training process. Specifically, a patch-based self-supervised embedding network is firstly devised as the base learner to learn an abstract representation of the observation from unlabeled samples. Then we apply few-shot learning and generate abstract representation related to prohibited items from support sample within the embedding space, which is followed by obtaining the corresponding class-specific prototype representations via masked average pooling. The distance between each pixel of query sample and prototypes are calculated to predict the label of each pixel. Moreover, prototype reverse validation strategy (PRV) is proposed to further exploit the support representation to assist training. Extensive experiments show that our proposed method outperforms the state-of-the-art by delivering a higher accuracy on automated prohibited items inspection and requiring less labeled samples.