Towards Full-parameter and Parameter-efficient Self-learning For Endoscopic Camera Depth Estimation
<|reference_start|>Towards Full-parameter and Parameter-efficient Self-learning For Endoscopic Camera Depth Estimation: Adaptation methods are developed to adapt depth foundation models to endoscopic depth estimation recently. However, such approaches typically under-perform training since they limit the parameter search to a low-rank subspace and alter the training dynamics. Therefore, we propose a full-parameter and parameter-efficient learning framework for endoscopic depth estimation. At the first stage, the subspace of attention, convolution and multi-layer perception are adapted simultaneously within different sub-spaces. At the second stage, a memory-efficient optimization is proposed for subspace composition and the performance is further improved in the united sub-space. Initial experiments on the SCARED dataset demonstrate that results at the first stage improves the performance from 10.2% to 4.1% for Sq Rel, Abs Rel, RMSE and RMSE log in the comparison with the state-of-the-art models.<|reference_end|>
@article{zhao2024towards,
    title={Towards Full-parameter and Parameter-efficient Self-learning For
  Endoscopic Camera Depth Estimation},
    author={Shuting Zhao, Chenkang Du, Kristin Qi, Xinrong Chen, and Xinhan Di},
    journal={arXiv preprint arXiv:2410.00979},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.00979},
    primaryClass={cs.CV cs.AI}
}