Unsupervised monocular depth estimation frameworks have shown promising
performance in autonomous driving. However, existing solutions primarily rely
on a simple convolutional neural network for ego-motion recovery, which
struggles to estimate precise camera poses in dynamic, complicated real-world
scenarios. These inaccurately estimated camera poses can inevitably deteriorate
the photometric reconstruction and mislead the depth estimation networks with
wrong supervisory signals. In this article, we introduce SCIPaD, a novel
approach that incorporates spatial clues for unsupervised depth-pose joint
learning. Specifically, a confidence-aware feature flow estimator is proposed
to acquire 2D feature positional translations and their associated confidence
levels. Meanwhile, we introduce a positional clue aggregator, which integrates
pseudo 3D point clouds from DepthNet and 2D feature flows into homogeneous
positional representations. Finally, a hierarchical positional embedding
injector is proposed to selectively inject spatial clues into semantic features
for robust camera pose decoding. Extensive experiments and analyses demonstrate
the superior performance of our model compared to other state-of-the-art
methods. Remarkably, SCIPaD achieves a reduction of 22.2\% in average
translation error and 34.8\% in average angular error for camera pose
estimation task on the KITTI Odometry dataset. Our source code is available at
https://mias.group/SCIPaD.

SCIPaD 是一种新颖的方法，它通过引入空间线索来进行无监督深度估计和相机姿态估计，并取得了比其他先进方法更优异的性能。

SCIPaD：将空间线索融入无监督姿态深度联合学习

SCIPaD: Incorporating Spatial Clues into Unsupervised Pose-Depth Joint  Learning

Generating paragraphs of diverse contents is important in many applications.
Existing generation models produce similar contents from homogenized contexts
due to the fixed left-to-right sentence order. Our idea is permuting the
sentence orders to improve the content diversity of multi-sentence paragraph.
We propose a novel framework PermGen whose objective is to maximize the
expected log-likelihood of output paragraph distributions with respect to all
possible sentence orders. PermGen uses hierarchical positional embedding and
designs new procedures for training, decoding, and candidate ranking in the
sentence-permuted generation. Experiments on three paragraph generation
benchmarks demonstrate PermGen generates more diverse outputs with a higher
quality than existing models.

本研究提出了一种叫 PermGen 的新框架，通过对句子顺序进行排列可以提高多句段落的内容多样性，并最大化输出段落分布的对数似然，PermGen 使用了分层位置嵌入和设计了新的程序进行训练、解码和候选排名，在三个段落生成基准测试中，PermGen 生成了比现有模型更多样化和高质量的输出。