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 (I^3Net), 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 I^3Net exceeds the state-of-the-art performance on benchmark datasets.

本文提出了一种适用于单级检测器的隐式实例不变网络（I3Net），通过利用不同层次深度特征的自然特点，从三个方面促进适应：动态和类平衡权重（DCBR）策略、类别感知对象模式匹配（COPM）模块和正则化联合类别对齐（RJCA）模块。实验结果表明，I3Net在基准数据集上优于现有技术的性能。

I3Net：隐式实例不变网络用于调整一阶段物体探测器