基于元学习的双目深度估计在线适应算法

Online Adaptation Through Meta-learning for Stereo Depth Estimation

双目深度估计的在线适应是一个有挑战性的问题,其要求模型能够在不断变化的目标场景中在线连续地自我调整并适应于当前环境.为处理该问题,提出一种新的在线元学习适应算法(Online meta-learning model with adaptation,OMLA),其贡献主要体现在两方面:首先引入在线特征对齐方法处理目标域和源域特征的分布偏差,以减少数据域转移的影响;然后利用在线元学习方法调整特征对齐过程和网络权重,使模型实现快速收敛.此外,提出一种新的基于元学习的预训练方法,以获得适用于在线学习场景的深度网络参数.相关实验分析表明,OMLA和元学习预训练算法均能帮助模型快速适应于新场景,在KITTI数据集上的实验对比表明,本文方法的效果超越了当前最佳的在线适应算法,接近甚至优于在目标域离线训练的理想模型.

This work tackles the problem of online adaptation for stereo depth estimation,that consists in continuously adapting a deep network to a target video recorded in an environment different from that of the source training set.To address this problem,we propose a novel online meta-learning model with adaptation(OMLA).Our proposal is based on two main contributions.First,to reduce the domain-shift between source and target feature distributions we introduce an online feature alignment procedure derived from batch normalization.Second,we devise a meta-learning approach that exploits feature alignment for faster convergence in an online learning setting.Additionally,we propose a meta-pre-training algorithm in order to obtain initial network weights on the source dataset which facilitate adaptation on future data streams.Experimentally,we show that both OMLA and meta-pre-training help the model to adapt faster to a new environment.Our proposal is evaluated on the KITTI dataset,where we show that our method outperforms both algorithms trained on the target data in an offline setting and state-ofthe-art adaptation methods.