Information Separation Network for Domain Adaptation Learning

Abstract

The Bai People have left behind a wealth of ancient texts that record their splendid civilization, unfortunately fewer and fewer people can read these texts in the present time. Therefore, it is of great practical value to design a model that can automatically recognize the Bai ancient (offset) texts. However, due to the expert knowledge involved in the annotation of ancient (offset) texts, and its limited scale, we consider that using handwritten Bai texts to help identify ancient (offset) Bai texts for handwritten Bai texts can be easily obtained and annotated. Essentially, this is a problem of domain adaptation, and some of the domain adaptation methods were transplanted to handle ancient (offset) Bai text recognition. Unfortunately, none of them succeeded in obtaining a high performance due to the fact that they do not solve the problem of how to separate the style and content information of an image. To address this, an information separation network (ISN) that can effectively separate content and style information and eventually classify with content features only, is proposed. Specifically, our network first divides the visual features into a style feature and a content feature by a separator, and ensures that the style feature contains only style and the content feature contains only content by cross-domain cross-reconstruction; thus, achieving the separation of style and content, and finally using only the content feature for classification. This greatly reduces the impact brought by cross-domain. The proposed method achieves leading results on five public datasets and a private one.

Domain Adaptive Subterranean 3D Pedestrian Detection via Instance Transfer and Confidence Guidance

“…In comparison to the subterranean scenes, abundant annotated point cloud data exist in the road scenes. It is observed that domain adaptation has the ability to transfer knowledge from the annotated scenes to the unannotated scenes [5,6]. Therefore, exploring an effective domain adaptive subterranean 3D pedestrian detection method for accurate 3D pedestrian detection in the target domain subterranean scenes requires further research.…”