Surveillance videos and images are used for a broad set of applications,
ranging from traffic analysis to crime detection. Extrinsic camera calibration
data is important for most analysis applications. However, security cameras are
susceptible to environmental conditions and small camera movements, resulting
in a need for an automated re-calibration method that can account for these
varying conditions. In this paper, we present an automated camera-calibration
process leveraging a dictionary-based approach that does not require prior
knowledge on any camera settings. The method consists of a custom
implementation of a Spatial Transformer Network (STN) and a novel topological
loss function. Experiments reveal that the proposed method improves the IoU
metric by up to 12% w.r.t. a state-of-the-art model across five synthetic
datasets and the World Cup 2014 dataset.

我们提出了一种基于字典的自动化摄像机校准方法，利用定制的空间变换网络（STN）和新颖的拓扑损失函数，改善了 IoU 指标，相对于最先进模型提高了 12％。

复杂拓扑场景中的单应性估计

Homography Estimation in Complex Topological Scenes

Classical epidemiological models assume homogeneous populations. There have
been important extensions to model heterogeneous populations, when the identity
of the sub-populations is known, such as age group or geographical location.
Here, we propose two new methods to model the number of people infected with
COVID-19 over time, each as a linear combination of latent sub-populations --
i.e., when we do not know which person is in which sub-population, and the only
available observations are the aggregates across all sub-populations. Method #1
is a dictionary-based approach, which begins with a large number of pre-defined
sub-population models (each with its own starting time, shape, etc), then
determines the (positive) weight of small (learned) number of sub-populations.
Method #2 is a mixture-of-$M$ fittable curves, where $M$, the number of
sub-populations to use, is given by the user. Both methods are compatible with
any parametric model; here we demonstrate their use with first (a)~Gaussian
curves and then (b)~SIR trajectories. We empirically show the performance of
the proposed methods, first in (i) modeling the observed data and then in (ii)
forecasting the number of infected people 1 to 4 weeks in advance. Across 187
countries, we show that the dictionary approach had the lowest mean absolute
percentage error and also the lowest variance when compared with classical SIR
models and moreover, it was a strong baseline that outperforms many of the
models developed for COVID-19 forecasting.

该研究提出了两种新的模型方法来预测 COVID-19 感染人数，分别为基于字典的方法和可拟合曲线的混合方法，通过子人群的线性组合进行建模，对 187 个国家的数据进行了实证研究，发现该字典方法在预测中表现最优。

利用潜在亚群建模和预测 COVID-19 病例

Modeling and Forecasting COVID-19 Cases using Latent Subpopulations

To address functional-output regression, we introduce projection learning
(PL), a novel dictionary-based approach that learns to predict a function that
is expanded on a dictionary while minimizing an empirical risk based on a
functional loss. PL makes it possible to use non orthogonal dictionaries and
can then be combined with dictionary learning; it is thus much more flexible
than expansion-based approaches relying on vectorial losses. This general
method is instantiated with reproducing kernel Hilbert spaces of vector-valued
functions as kernel-based projection learning (KPL). For the functional square
loss, two closed-form estimators are proposed, one for fully observed output
functions and the other for partially observed ones. Both are backed
theoretically by an excess risk analysis. Then, in the more general setting of
integral losses based on differentiable ground losses, KPL is implemented using
first-order optimization for both fully and partially observed output
functions. Eventually, several robustness aspects of the proposed algorithms
are highlighted on a toy dataset; and a study on two real datasets shows that
they are competitive compared to other nonlinear approaches. Notably, using the
square loss and a learnt dictionary, KPL enjoys a particularily attractive
trade-off between computational cost and performances.

介绍了一种基于字典的方法 —— 投影学习，用于解决功能输出回归问题，该方法使用不正交字典进行函数预测，可以与字典学习相结合，因此比基于向量的方法更加灵活。采用向量值函数的再生核希尔伯特空间作为基础的核投影学习具有良好的性能。