We address the problem of camera pose estimation in visual localization.
Current regression-based methods for pose estimation are trained and evaluated
scene-wise. They depend on the coordinate frame of the training dataset and
show a low generalization across scenes and datasets. We identify the dataset
shift an important barrier to generalization and consider transfer learning as
an alternative way towards a better reuse of pose estimation models. We revise
domain adaptation techniques for classification and extend them to camera pose
estimation, which is a multi-regression task. We develop a deep adaptation
network for learning scene-invariant image representations and use adversarial
learning to generate such representations for model transfer. We enrich the
network with self-supervised learning and use the adaptability theory to
validate the existence of scene-invariant representation of images in two given
scenes. We evaluate our network on two public datasets, Cambridge Landmarks and
7Scene, demonstrate its superiority over several baselines and compare to the
state of the art methods.

我们提出了一种基于迁移学习和领域自适应技术的深度适应网络，通过自监督学习获取具有场景不变性的图像表示，并将其用于摄像机姿态估计中。在 Cambridge Landmarks 和 7Scene 上的评估表明，该网络优于几个基线，并与最先进的方法进行了比较。

姿态估计回归的对抗转移

Adversarial Transfer of Pose Estimation Regression

Recent studies reveal that a deep neural network can learn transferable
features which generalize well to novel tasks for domain adaptation. However,
as deep features eventually transition from general to specific along the
network, the feature transferability drops significantly in higher layers with
increasing domain discrepancy. Hence, it is important to formally reduce the
dataset bias and enhance the transferability in task-specific layers. In this
paper, we propose a new Deep Adaptation Network (DAN) architecture, which
generalizes deep convolutional neural network to the domain adaptation
scenario. In DAN, hidden representations of all task-specific layers are
embedded in a reproducing kernel Hilbert space where the mean embeddings of
different domain distributions can be explicitly matched. The domain
discrepancy is further reduced using an optimal multi-kernel selection method
for mean embedding matching. DAN can learn transferable features with
statistical guarantees, and can scale linearly by unbiased estimate of kernel
embedding. Extensive empirical evidence shows that the proposed architecture
yields state-of-the-art image classification error rates on standard domain
adaptation benchmarks.

本文提出了一种新的深度自适应网络 (DAN) 结构，它将多个领域的分布嵌入到再生核 Hilbert 空间中，并应用最优多核选择方法以匹配均值嵌入。DAN 能够学习具有统计保证的可传递特征，并通过核嵌入的无偏估计来实现线性扩展。实验证据表明，所提出的架构在标准领域自适应基准数据集上获得了最先进的图像分类误差率。