Versatile Teacher: A Class-aware Teacher-student Framework for Cross-domain Adaptation
<|reference_start|>Versatile Teacher: A Class-aware Teacher-student Framework for Cross-domain Adaptation: Addressing the challenge of domain shift between datasets is vital in maintaining model performance. In the context of cross-domain object detection, the teacher-student framework, a widely-used semi-supervised model, has shown significant accuracy improvements. However, existing methods often overlook class differences, treating all classes equally, resulting in suboptimal results. Furthermore, the integration of instance-level alignment with a one-stage detector, essential due to the absence of a Region Proposal Network (RPN), remains unexplored in this framework. In response to these shortcomings, we introduce a novel teacher-student model named Versatile Teacher (VT). VT differs from previous works by considering class-specific detection difficulty and employing a two-step pseudo-label selection mechanism, referred to as Class-aware Pseudo-label Adaptive Selection (CAPS), to generate more reliable pseudo labels. These labels are leveraged as saliency matrices to guide the discriminator for targeted instance-level alignment. Our method demonstrates promising results on three benchmark datasets, and extends the alignment methods for widely-used one-stage detectors, presenting significant potential for practical applications. Code is available at https://github.com/RicardooYoung/VersatileTeacher.<|reference_end|>
@article{yang2024versatile,
    title={Versatile Teacher: A Class-aware Teacher-student Framework for
  Cross-domain Adaptation},
    author={Runou Yang, Tian Tian, Jinwen Tian},
    journal={arXiv preprint arXiv:2405.11754},
    year={2024},
    doi={10.1016/j.patcog.2024.111024},
    archivePrefix={arXiv},
    eprint={2405.11754},
    primaryClass={cs.CV}
}