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DOMAIN ADAPTATION OF OBJECT DETECTORS USING THE TEACHER MODEL WITH SUB ENSEMBLES

Medvedev Anatolii Andreevich  (PhD Student ITMO University )

Domain adaptation for object detection remains a critical issue in computer vision, especially when models trained in the same domain are deployed to significantly different target domains. Traditional approaches, such as fine-tuning using labeled target domain data or using uniform teacher-student designs, often fail to cope with the complexity and variability of real-world scenarios. This paper presents a novel Sub-Ensemble Teacher (SET) approach that leverages the strengths of multiple teacher models to provide robust and comprehensive guidance to the student model for domain adaptation. The SET framework involves training multiple teacher models on different subsets of domain input data, each representing different aspects of the domain. These sub-ensembles generate a rich set of pseudo-labels for the unlabeled target domain data, which are then used to train the student model. This ensemble method improves the robustness and generalizability of the student model by efficiently approximating Bayesian inference, accounting for model uncertainty, and reducing label noise. Experimental results on benchmark datasets show that the SET approach significantly outperforms traditional single-supervised models on cross-domain object detection tasks. The subensemble technique not only improves detection accuracy, but also provides a more robust estimate of uncertainty, making it a powerful tool for adapting object detectors to diverse and complex environments.

Keywords:object detection, domain adaptation, computer vision, deep learning, one-stage object detectors, sub-ensembles, object localization, uncertainty estimation.
 

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Citation link:
Medvedev A. A. DOMAIN ADAPTATION OF OBJECT DETECTORS USING THE TEACHER MODEL WITH SUB ENSEMBLES // Современная наука: актуальные проблемы теории и практики. Серия: Естественные и Технические Науки. -2024. -№08. -С. 113-118 DOI 10.37882/2223-2966.2024.8.21
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