Background subtraction (BGS) aims to extract all moving objects in the video frames to obtain binary foreground segmentation masks. Deep learning has been widely used in this field. Compared with supervised-based BGS methods, unsupervised methods have better generalization. However, previous unsupervised deep learning BGS algorithms perform poorly in sophisticated scenarios such as shadows or night lights, and they cannot detect objects outside the pre-defined categories. In this work, we propose an unsuper-vised BGS algorithm based on zero-shot object detection called Zero-shot Background Subtraction (ZBS). The proposed method fully utilizes the advantages of zero-shot object detection to build the open-vocabulary instance-level background model. Based on it, the foreground can be effectively extracted by comparing the detection results of new frames with the background model. ZBS performs well for sophisticated scenarios, and it has rich and extensible categories. Furthermore, our method can easily generalize to other tasks, such as abandoned object detection in unseen environments. We experimentally show that ZBS surpasses state-of-the-art unsupervised BGS methods by 4.70% F-Measure on the CDnet 2014 dataset. The code is released at https://github.com/CASIA-IVA-Lab/ZBS.

As a prerequisite for EMI (English as Medium of Instruction) specialized courses, ESP (English for Specific Purposes) is supposed to help the engineering students learn the academic vocabulary and terms, discuss the related topics, and improve their research skills. Besides, comparing to the EMI specialized courses in which the teachers usually have pressure to cover a lot of academic contents, ESP might be the most suitable course for Chinese engineering students to improve the interacting and presenting skills in English. Nowadays, WeChat is the most popular social app in China. WeChat public platform provides an efficient way to share and spread information. In order to enhance the teaching effect and the student engagement, two WeChat-based interactive tools, Rain classroom and Formtalk, were adopted in the ESP classroom for 40 electronic engineering students. The students' feedbacks were also collected by the interactive tools before, during and after the lectures. The purpose of this paper is to describe the methods and share the experiences.