  Domain adaptive detection aims to improve the generalization of detectors on
target domain. To reduce discrepancy in feature distributions between two
domains, recent approaches achieve domain adaption through feature alignment in
different granularities via adversarial learning. However, they neglect the
relationship between multiple granularities and different features in
alignment, degrading detection. Addressing this, we introduce a unified
multi-granularity alignment (MGA)-based detection framework for
domain-invariant feature learning. The key is to encode the dependencies across
different granularities including pixel-, instance-, and category-levels
simultaneously to align two domains. Specifically, based on pixel-level
features, we first develop an omni-scale gated fusion (OSGF) module to
aggregate discriminative representations of instances with scale-aware
convolutions, leading to robust multi-scale detection. Besides, we introduce
multi-granularity discriminators to identify where, either source or target
domains, different granularities of samples come from. Note that, MGA not only
leverages instance discriminability in different categories but also exploits
category consistency between two domains for detection. Furthermore, we present
an adaptive exponential moving average (AEMA) strategy that explores model
assessments for model update to improve pseudo labels and alleviate local
misalignment problem, boosting detection robustness. Extensive experiments on
multiple domain adaption scenarios validate the superiority of MGA over other
approaches on FCOS and Faster R-CNN detectors. Code will be released at
https://github.com/tiankongzhang/MGA.
