Attention-Based Class-Conditioned Alignment for Multi-Source Domain Adaptation of Object Detectors

Atif Belal, Akhil Meethal, Francisco Perdigon Romero, Marco Pedersoli, Eric Granger; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 8555-8564

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 modality 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 caused by 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 designed to align instances of each object category across domains. In particular an attention module combined 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 state-of-the-art methods and exhibits robustness to class imbalance achieved through a conceptually simple class-conditioning strategy. Our code is available at: https://github.com/imatif17/ACIA.