Dual Teacher: A Semisupervised Cotraining Framework for Cross-Domain Ship Detection

Abstract

Cross-domain ship detection tries to identify Synthetic Aperture Radar (SAR) ship by adapting knowledge from labeled optical images, without labor-intensive annotations. In practical applications, a few ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., one or three samples) labeled SAR samples are available, which provides an additional supervision for SAR ships. However, the existing cross-domain methods ignore the SAR supervision (a few labeled and unlabeled SAR images), which limits their performances in a practical and under-investigated task: semi-supervised cross-domain ship detection. In this paper, a Dual Teacher framework is proposed to address the mutual interference between the optical supervision and the SAR supervision. First, both optical and SAR supervision are decomposed into two sub-tasks: cross-domain task and semi-supervised task. Then, both cross-domain task and semi-supervised task can be learned interactively in two individual teacher-student models. The teacher-student models generate pseudo-labels on unlabeled SAR images by a teacher network and fine-tune the student network. Finally, the Dual Teacher framework retrains two teacher-student models in co-training strategies. Both cross-domain dataset and semi-supervised dataset are exploited to jointly improve the pseudo-label quality. The effectiveness of the Dual Teacher framework has been fully experimentally demonstrated. The code is available at https://github.com/XiangtaoZheng/DualTeacher.