  Domain adaptive object detection is challenging due to distinctive data
distribution between source domain and target domain. In this paper, we propose
a unified multi-granularity alignment based object detection framework towards
domain-invariant feature learning. To this end, we encode the dependencies
across different granularity perspectives including pixel-, instance-, and
category-levels simultaneously to align two domains. Based on pixel-level
feature maps from the backbone network, we first develop the omni-scale gated
fusion module to aggregate discriminative representations of instances by
scale-aware convolutions, leading to robust multi-scale object detection.
Meanwhile, the multi-granularity discriminators are proposed to identify which
domain different granularities of samples(i.e., pixels, instances, and
categories) come from. Notably, we leverage not only the instance
discriminability in different categories but also the category consistency
between two domains. Extensive experiments are carried out on multiple domain
adaptation scenarios, demonstrating the effectiveness of our framework over
state-of-the-art algorithms on top of anchor-free FCOS and anchor-based Faster
RCNN detectors with different backbones.
