Domain adaptation helps generalizing object detection models to target domain data with distribution shift. It is often achieved by adapting with access to the whole target domain data. In a more realistic scenario, target distribution is often unpredictable until inference stage. This motivates us to explore adapting an object detection model at test-time, a.k.a. test-time adaptation (TTA). In this work, we approach test-time adaptive object detection (TTAOD) from two perspective. First, we adopt a self-training paradigm to generate pseudo labeled objects with an exponential moving average model. The pseudo labels are further used to supervise adapting source domain model. As self-training is prone to incorrect pseudo labels, we further incorporate aligning feature distributions at two output levels as regularizations to self-training. To validate the performance on TTAOD, we create benchmarks based on three standard object detection datasets and adapt generic TTA methods to object detection task. Extensive evaluations suggest our proposed method sets the state-of-the-art on test-time adaptive object detection task.

翻译：领域自适应有助于将物体检测模型推广到具有分布偏移的目标域数据。通常通过可访问整个目标域数据进行适应来实现。更现实的场景是，在推理阶段目标分布往往是不可预测的。这激励我们从两个角度探索在测试时间内适应目标数据的物体检测模型(TTAOD)。首先，我们采用自训练范式使用指数移动平均模型生成伪标记对象。伪标签进一步用于监督适应源域模型。由于自我训练易受不正确的伪标签的影响，因此我们进一步将两个输出级别的特征分布对齐作为自我训练的正则化项。为了验证在 TTAOD 上的性能，我们基于三个标准物体检测数据集创建了基准。广泛的评估表明，我们提出的方法在测试时自适应物体检测任务上树立了业界标准。