Domain adaptation methods face performance degradation in object detection,
as the complexity of tasks require more about the transferability of the model.
We propose a new perspective on how CNN models gain the transferability,
viewing the weights of a model as a series of motion patterns. The directions
of weights, and the gradients, can be divided into domain-specific and
domain-invariant parts, and the goal of domain adaptation is to concentrate on
the domain-invariant direction while eliminating the disturbance from
domain-specific one. Current UDA object detection methods view the two
directions as a whole while optimizing, which will cause domain-invariant
direction mismatch even if the output features are perfectly aligned. In this
paper, we propose the domain-specific suppression, an exemplary and
generalizable constraint to the original convolution gradients in
backpropagation to detach the two parts of directions and suppress the
domain-specific one. We further validate our theoretical analysis and methods
on several domain adaptive object detection tasks, including weather, camera
configuration, and synthetic to real-world adaptation. Our experiment results
show significant advance over the state-of-the-art methods in the UDA object
detection field, performing a promotion of $10.2\sim12.2\%$ mAP on all these
domain adaptation scenarios.

该研究提出了一种新的 CNN 模型迁移多样性学习的方法，将权重视为一系列运动模式，通过抑制特定领域的干扰，并集中在领域不变方向上，成功地在多个对象检测任务中实现了显著的性能提升。