Existing approaches for unsupervised domain adaptive object detection perform feature alignment via adversarial training. While these methods achieve reasonable improvements in performance, they typically perform category-agnostic domain alignment, thereby resulting in negative transfer of features. To overcome this issue, in this work, we attempt to incorporate category information into the domain adaptation process by proposing Memory Guided Attention for Category-Aware Domain Adaptation (MeGA-CDA). The proposed method consists of employing category-wise discriminators to ensure category-aware feature alignment for learning domain-invariant discriminative features. However, since the category information is not available for the target samples, we propose to generate memory-guided category-specific attention maps which are then used to route the features appropriately to the corresponding category discriminator. The proposed method is evaluated on several benchmark datasets and is shown to outperform existing approaches.

本研究提出了一种名为 MeGA-CDA 的基于 Memory Guided Attention 的领域自适应物体检测方法，能够通过使用分类特定的鉴别器和生成的内存引导类别特定的注意力地图，实现分类感知的特征对齐与学习领域不变的鉴别特征，表现出优越的性能。

MeGA-CDA: 基于记忆引导的注意力机制的类别感知无监督域自适应物体检测