Existing self-supervised monocular depth estimation methods can get rid of
expensive annotations and achieve promising results. However, these methods
suffer from severe performance degradation when directly adopting a model
trained on a fixed resolution to evaluate at other different resolutions. In
this paper, we propose a resolution adaptive self-supervised monocular depth
estimation method (RA-Depth) by learning the scale invariance of the scene
depth. Specifically, we propose a simple yet efficient data augmentation method
to generate images with arbitrary scales for the same scene. Then, we develop a
dual high-resolution network that uses the multi-path encoder and decoder with
dense interactions to aggregate multi-scale features for accurate depth
inference. Finally, to explicitly learn the scale invariance of the scene
depth, we formulate a cross-scale depth consistency loss on depth predictions
with different scales. Extensive experiments on the KITTI, Make3D and NYU-V2
datasets demonstrate that RA-Depth not only achieves state-of-the-art
performance, but also exhibits a good ability of resolution adaptation.

本研究提出了一种分辨率自适应的自监督单目深度估计方法，通过数据增强和多路径编码器解码器等方法提高特征融合精度，进而在 KITTI，Make3D 和 NYU-V2 等数据集上取得了具有竞争力的结果。

RA-Depth: 分辨率自适应自监督单目深度估计

RA-Depth: Resolution Adaptive Self-Supervised Monocular Depth Estimation

Person re-identification (re-ID) solves the task of matching images across
cameras and is among the research topics in vision community. Since query
images in real-world scenarios might suffer from resolution loss, how to solve
the resolution mismatch problem during person re-ID becomes a practical
problem. Instead of applying separate image super-resolution models, we propose
a novel network architecture of Resolution Adaptation and re-Identification
Network (RAIN) to solve cross-resolution person re-ID. Advancing the strategy
of adversarial learning, we aim at extracting resolution-invariant
representations for re-ID, while the proposed model is learned in an end-to-end
training fashion. Our experiments confirm that the use of our model can
recognize low-resolution query images, even if the resolution is not seen
during training. Moreover, the extension of our model for semi-supervised re-ID
further confirms the scalability of our proposed method for real-world
scenarios and applications.

本文提出一种名为 RAIN 的网络架构，通过对抗学习的策略来提取具有分辨率不变性的表示，从而解决跨分辨率人物重新识别的问题，实验表明所提出的模型可以识别低分辨率的查询图像，并可以用于半监督重新识别。