  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 .
