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。