  Recent works on two-stage cross-domain detection have widely explored the
local feature patterns to achieve more accurate adaptation results. These
methods heavily rely on the region proposal mechanisms and ROI-based
instance-level features to design fine-grained feature alignment modules with
respect to the foreground objects. However, for one-stage detectors, it is hard
or even impossible to obtain explicit instance-level features in the detection
pipelines. Motivated by this, we propose an Implicit Instance-Invariant Network
(I3Net), which is tailored for adapting one-stage detectors and implicitly
learns instance-invariant features via exploiting the natural characteristics
of deep features in different layers. Specifically, we facilitate the
adaptation from three aspects: (1) Dynamic and Class-Balanced Reweighting
(DCBR) strategy, which considers the coexistence of intra-domain and
intra-class variations to assign larger weights to those sample-scarce
categories and easy-to-adapt samples; (2) Category-aware Object Pattern
Matching (COPM) module, which boosts the cross-domain foreground objects
matching guided by the categorical information and suppresses the uninformative
background features; (3) Regularized Joint Category Alignment (RJCA) module,
which jointly enforces the category alignment at different domain-specific
layers with a consistency regularization. Experiments reveal that I3Net exceeds
the state-of-the-art performance on benchmark datasets.
