提出全参数高效自学习框架，提升内窥镜深度估计精度
Towards Full-parameter and Parameter-efficient Self-learning For Endoscopic Camera Depth Estimation
- 分阶段优化注意力、卷积与MLP子空间，实现全参数适配
- 在SCARED数据集上，误差指标下降至4.1%（原为10.2%）
- 适合需要高精度深度估计的医疗视觉任务研究者
近期研究通过适配深度基础模型来解决内窥镜深度估计问题，但这类方法通常因限制参数搜索范围至低秩子空间而性能不足，并改变训练动态。为此，本文提出一种全参数且参数高效的自学习框架。第一阶段同时在不同子空间中适应注意力、卷积和多层感知机模块；第二阶段采用内存高效的优化策略，对子空间进行组合，进一步提升性能。在SCARED数据集上的初步实验表明，相较于现有最优模型，该方法在Sq Rel、Abs Rel、RMSE和RMSE log四项指标上分别从10.2%降至4.1%，显著提升精度。
原文摘要 · Abstract (English)
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.
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