Exploring Test-Time Adaptation for Object Detection in Continually Changing Environments
<|reference_start|>Exploring Test-Time Adaptation for Object Detection in Continually Changing Environments: Real-world application models are commonly deployed in dynamic environments, where the target domain distribution undergoes temporal changes. Continual Test-Time Adaptation (CTTA) has recently emerged as a promising technique to gradually adapt a source-trained model to continually changing target domains. Despite recent advancements in addressing CTTA, two critical issues remain: 1) Fixed thresholds for pseudo-labeling in existing methodologies generate low-quality pseudo-labels, as model confidence varies across categories and domains; 2) Stochastic parameter restoration methods for mitigating catastrophic forgetting fail to effectively preserve critical information due to their intrinsic randomness. To tackle these challenges for detection models in CTTA scenarios, we present CTAOD, featuring three core components. Firstly, the object-level contrastive learning module extracts object-level features for contrastive learning to refine the feature representation in the target domain. Secondly, the adaptive monitoring module dynamically skips unnecessary adaptation and updates the category-specific threshold based on predicted confidence scores to enable efficiency and improve the quality of pseudo-labels. Lastly, the data-driven stochastic restoration mechanism selectively reset inactive parameters with higher possibilities, ensuring the retention of essential knowledge. We demonstrate the effectiveness of CTAOD on four CTTA object detection tasks, where CTAOD outperforms existing methods, especially achieving a 3.2 mAP improvement and a 20% increase in efficiency on the Cityscapes-to-Cityscapes-C CTTA task. The code will be released.<|reference_end|>
@article{cao2024exploring,
    title={Exploring Test-Time Adaptation for Object Detection in Continually
  Changing Environments},
    author={Shilei Cao, Yan Liu, Juepeng Zheng, Weijia Li, Runmin Dong, Haohuan Fu},
    journal={arXiv preprint arXiv:2406.16439},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.16439},
    primaryClass={cs.CV}
}