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.

本文中，我们提出了一个统一的多粒度对齐的对象检测框架，以实现领域内不变的特征学习。我们使用来自主干网络的像素级特征图，首先开发了全尺度门控融合模块来聚合实例的判别表示，从而实现了强大的多尺度目标检测。与此同时，我们提出了多粒度鉴别器，以识别样本（即像素、实例和类别）不同粒度来自哪个领域。在不同的背骨骼架上，我们进行了广泛的实验，证明了我们的框架在锚点自由 FCOS 和锚点 Faster RCNN 检测器上的有效性。