While Domain Adaptive Object Detection (DAOD) has made significant strides, most methods rely on unlabeled target data that is assumed to contain sufficient foreground instances. However, in many practical scenarios (e.g., wildlife monitoring, lesion detection), collecting target domain data with objects of interest is prohibitively costly, whereas background-only data is abundant. This common practical constraint introduces a significant technical challenge: the difficulty of achieving domain alignment when target instances are unavailable, forcing adaptation to rely solely on the target background information. We formulate this challenge as the novel problem of Instance-Free Domain Adaptive Object Detection. To tackle this, we propose the Relational and Structural Consistency Network (RSCN) which pioneers an alignment strategy based on background feature prototypes while simultaneously encouraging consistency in the relationship between the source foreground features and the background features within each domain, enabling robust adaptation even without target instances. To facilitate research, we further curate three specialized benchmarks, including simulative auto-driving detection, wildlife detection, and lung nodule detection. Extensive experiments show that RSCN significantly outperforms existing DAOD methods across all three benchmarks in the instance-free scenario. The code and benchmarks will be released soon.

翻译：尽管域自适应目标检测（DAOD）已取得显著进展，但现有方法大多依赖于未标记的目标域数据，并假设其中包含足够的前景实例。然而，在许多实际场景（例如野生动物监测、病灶检测）中，采集包含感兴趣对象的目标域数据成本极高，而仅包含背景的数据却十分丰富。这一常见的实际约束带来了重大的技术挑战：当目标实例不可用时，实现域对齐变得极为困难，迫使自适应过程只能依赖于目标背景信息。我们将这一挑战形式化为一个新颖的问题：实例无关的域自适应目标检测。为解决此问题，我们提出了关系与结构一致性网络（RSCN），该网络首创了一种基于背景特征原型的对齐策略，同时鼓励每个域内源前景特征与背景特征之间关系的一致性，从而即使在没有目标实例的情况下也能实现鲁棒的自适应。为促进相关研究，我们进一步构建了三个专用基准数据集，包括模拟自动驾驶检测、野生动物检测和肺结节检测。大量实验表明，在实例无关场景下，RSCN在所有三个基准数据集上均显著优于现有的DAOD方法。代码与基准数据集即将发布。