Online Learning via Memory: Retrieval-Augmented Detector Adaptation
<|reference_start|>Online Learning via Memory: Retrieval-Augmented Detector Adaptation: This paper presents a novel way of online adapting any off-the-shelf object detection model to a novel domain without retraining the detector model. Inspired by how humans quickly learn knowledge of a new subject (e.g., memorization), we allow the detector to look up similar object concepts from memory during test time. This is achieved through a retrieval augmented classification (RAC) module together with a memory bank that can be flexibly updated with new domain knowledge. We experimented with various off-the-shelf open-set detector and close-set detectors. With only a tiny memory bank (e.g., 10 images per category) and being training-free, our online learning method could significantly outperform baselines in adapting a detector to novel domains.<|reference_end|>
@article{jian2024online,
    title={Online Learning via Memory: Retrieval-Augmented Detector Adaptation},
    author={Yanan Jian, Fuxun Yu, Qi Zhang, William Levine, Brandon Dubbs,
  Nikolaos Karianakis},
    journal={arXiv preprint arXiv:2409.10716},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10716},
    primaryClass={cs.CV cs.IR cs.LG}
}