Attention-based Class-Conditioned Alignment for Multi-Source Domain Adaptation of Object Detectors
<|reference_start|>Attention-based Class-Conditioned Alignment for Multi-Source Domain Adaptation of Object Detectors: 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.<|reference_end|>
@article{belal2024attention-based,
    title={Attention-based Class-Conditioned Alignment for Multi-Source Domain
  Adaptation of Object Detectors},
    author={Atif Belal and Akhil Meethal and Francisco Perdigon Romero and Marco
  Pedersoli and Eric Granger},
    journal={arXiv preprint arXiv:2403.09918},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.09918},
    primaryClass={cs.CV cs.LG}
}