Attention-based Class-Conditioned Alignment for Multi-Source Domain
  Adaptive Object Detection
Atif Belal Akhil Meethal Francisco Perdigon Romero Marco Pedersoli Eric Granger
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 which can negatively affect adaptation with imbalanced data. To
overcome these limitations, we propose an attention-based class-conditioned
alignment scheme 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. Our code is available at https://github.com/imatif17/ACIA.