  Domain adaptation methods for object detection (OD) strive to mitigate the
impact of distribution shifts by promoting feature alignment across source and
target domains. Multi-source domain adaptation (MSDA) allows leveraging
multiple annotated source datasets and unlabeled target data to improve the
accuracy and robustness of the detection model. Most state-of-the-art MSDA
methods for OD perform feature alignment in a class-agnostic manner. This is
challenging since the objects have unique modal information due to variations
in object appearance across domains. A recent prototype-based approach proposed
a class-wise alignment, yet it suffers from error accumulation due to noisy
pseudo-labels that can negatively affect adaptation with imbalanced data. To
overcome these limitations, we propose an attention-based class-conditioned
alignment method for MSDA that aligns instances of each object category across
domains. In particular, an attention module coupled with an adversarial domain
classifier allows learning domain-invariant and class-specific instance
representations. Experimental results on multiple benchmarking MSDA datasets
indicate that our method outperforms the state-of-the-art methods and is robust
to class imbalance using a conceptually simple class-conditioning method. Our
code is available at https://github.com/imatif17/ACIA.
