This paper presents a learning-based method for multi-view depth estimation
from posed images. Our core idea is a "learning-to-optimize" paradigm that
iteratively indexes a plane-sweeping cost volume and regresses the depth map
via a convolutional Gated Recurrent Unit (GRU). Since the cost volume plays a
paramount role in encoding the multi-view geometry, we aim to improve its
construction both at pixel- and frame- levels. At the pixel level, we propose
to break the symmetry of the Siamese network (which is typically used in MVS to
extract image features) by introducing a transformer block to the reference
image (but not to the source images). Such an asymmetric volume allows the
network to extract global features from the reference image to predict its
depth map. Given potential inaccuracies in the poses between reference and
source images, we propose to incorporate a residual pose network to correct the
relative poses. This essentially rectifies the cost volume at the frame level.
We conduct extensive experiments on real-world MVS datasets and show that our
method achieves state-of-the-art performance in terms of both within-dataset
evaluation and cross-dataset generalization.

本文提出了一种基于学习的方法，通过迭代索引平面扫描成本体积并借助卷积门控循环单元来回归深度图，从而进行多视角深度估计。经过对真实世界多视图数据集的大量实验，验证了该方法在数据集内部性能和跨数据集概括性能方面都达到了最先进水平。

RIAV-MVS: 基于循环索引的非对称体积多视角立体重建

RIAV-MVS: Recurrent-Indexing an Asymmetric Volume for Multi-View Stereo

Anomaly detection has been a challenging task given high-dimensional
multivariate time series data generated by networked sensors and actuators in
Cyber-Physical Systems (CPS). Besides the highly nonlinear, complex, and
dynamic natures of such time series, the lack of labeled data impedes data
exploitation in a supervised manner and thus prevents an accurate detection of
abnormal phenomenons. On the other hand, the collected data at the edge of the
network is often privacy sensitive and large in quantity, which may hinder the
centralized training at the main server. To tackle these issues, we propose an
unsupervised time series anomaly detection framework in a federated fashion to
continuously monitor the behaviors of interconnected devices within a network
and alerts for abnormal incidents so that countermeasures can be taken before
undesired consequences occur. To be specific, we leave the training data
distributed at the edge to learn a shared Variational Autoencoder (VAE) based
on Convolutional Gated Recurrent Unit (ConvGRU) model, which jointly captures
feature and temporal dependencies in the multivariate time series data for
representation learning and downstream anomaly detection tasks. Experiments on
three real-world networked sensor datasets illustrate the advantage of our
approach over other state-of-the-art models. We also conduct extensive
experiments to demonstrate the effectiveness of our detection framework under
non-federated and federated settings in terms of overall performance and
detection latency.

本研究提出了一种基于联邦学习的时序异常检测框架，采用分布式的共享变分自编码器（VAE）结合卷积门循环单元（ConvGRU）模型，对网络传感器产生的高维多元时序数据进行表征学习和异常检测任务。实验表明，该方法在综合性能和检测延迟方面比其他最先进的模型具有优势。

联邦变分学习用于多元时间序列异常检测

Federated Variational Learning for Anomaly Detection in Multivariate  Time Series

Segmenting salient objects in an image is an important vision task with
ubiquitous applications. The problem becomes more challenging in the presence
of a cluttered and textured background, low resolution and/or low contrast
images. Even though existing algorithms perform well in segmenting most of the
object(s) of interest, they often end up segmenting false positives due to
resembling salient objects in the background. In this work, we tackle this
problem by iteratively attending to image patches in a recurrent fashion and
subsequently enhancing the predicted segmentation mask. Saliency features are
estimated independently for every image patch, which are further combined using
an aggregation strategy based on a Convolutional Gated Recurrent Unit (ConvGRU)
network. The proposed approach works in an end-to-end manner, removing
background noise and false positives incrementally. Through extensive
evaluation on various benchmark datasets, we show superior performance to the
existing approaches without any post-processing.

该研究通过迭代地关注图像补丁并进一步增强预测的分割掩模，使用基于 ConvGRU 网络的聚合策略独立估算每个图像补丁的显著性特征，实现端到端的图像分割，较现有方法表现更优，消除背景噪声和假阳性。