Domain adaptation for object detection typically entails transferring knowledge from one visible domain to another visible domain. However, there are limited studies on adapting from the visible to the thermal domain, because the domain gap between the visible and thermal domains is much larger than expected, and traditional domain adaptation can not successfully facilitate learning in this situation. To overcome this challenge, we propose a Distinctive Dual-Domain Teacher (D3T) framework that employs distinct training paradigms for each domain. Specifically, we segregate the source and target training sets for building dual-teachers and successively deploy exponential moving average to the student model to individual teachers of each domain. The framework further incorporates a zigzag learning method between dual teachers, facilitating a gradual transition from the visible to thermal domains during training. We validate the superiority of our method through newly designed experimental protocols with well-known thermal datasets, i.e., FLIR and KAIST. Source code is available at https://github.com/EdwardDo69/D3T .

翻译：目标检测的域自适应通常涉及从可见光域向另一个可见光域的知识迁移。然而，由于可见光域与热红外域之间存在远超预期的巨大域间差异，传统域自适应方法无法有效支持这种跨模态学习，因此针对可见光到热红外域的自适应研究十分有限。为攻克这一难题，我们提出了一种独特的双域教师（D3T）框架，该框架为每个域采用不同的训练范式。具体而言，我们将源域与目标域的训练集分离以构建双教师模型，并依次对每个域的教师模型采用指数移动平均策略更新学生模型。该框架进一步引入双教师间的"之字形"学习方法，在训练过程中实现从可见光域到热红外域的渐进式过渡。我们通过基于知名热红外数据集（FLIR和KAIST）设计的新实验协议验证了本方法的优越性。源代码已发布于https://github.com/EdwardDo69/D3T。