  Building reliable object detectors that are robust to domain shifts, such as
various changes in context, viewpoint, and object appearances, is critical for
real-world applications. In this work, we study the effectiveness of auxiliary
self-supervised tasks to improve the out-of-distribution generalization of
object detectors. Inspired by the principle of maximum entropy, we introduce a
novel self-supervised task, instance-level temporal cycle confusion (CycConf),
which operates on the region features of the object detectors. For each object,
the task is to find the most different object proposals in the adjacent frame
in a video and then cycle back to itself for self-supervision. CycConf
encourages the object detector to explore invariant structures across instances
under various motions, which leads to improved model robustness in unseen
domains at test time. We observe consistent out-of-domain performance
improvements when training object detectors in tandem with self-supervised
tasks on large-scale video datasets (BDD100K and Waymo open data). The joint
training framework also establishes a new state-of-the-art on standard
unsupervised domain adaptative detection benchmarks (Cityscapes, Foggy
Cityscapes, and Sim10K). The code and models are available at
https://github.com/xinw1012/cycle-confusion.
