Trajectory generation is an important task in movement studies; it
circumvents the privacy, ethical, and technical challenges of collecting real
trajectories from the target population. In particular, real trajectories in
the wildlife domain are scarce as a result of ethical and environmental
constraints of the collection process. In this paper, we consider the problem
of generating long-horizon trajectories, akin to wildlife migration, based on a
small set of real samples. We propose a hierarchical approach to learn the
global movement characteristics of the real dataset and recursively refine
localized regions. Our solution, WildGraph, discretizes the geographic path
into a prototype network of H3 (this https URL) regions and
leverages a recurrent variational auto-encoder to probabilistically generate
paths over the regions, based on occupancy. WildGraph successfully generates
realistic months-long trajectories using a sample size as small as 60.
Experiments performed on two wildlife migration datasets demonstrate that our
proposed method improves the generalization of the generated trajectories in
comparison to existing work while achieving superior or comparable performance
in several benchmark metrics. Our code is published on the following
repository: \url{this https URL}.

研究了利用一小组真实样本生成类似野生动物迁徙的长期轨迹的问题，提出了一种分层方法来学习真实数据集的全局运动特征并递归改进本地化区域，通过使用 H3 区域的原型网络和经验变分自编码器基于占用概率，我们的解决方案 WildGraph 成功地生成了具有现实感的长达数月的轨迹。在两个野生动物迁徙数据集上进行的实验表明，与现有工作相比，我们提出的方法在生成轨迹的泛化能力上取得了改进，同时在几项基准指标上实现了更高或相当的性能。

野外图形：野生动物的现实图形轨迹生成

WildGraph: Realistic Graph-based Trajectory Generation for Wildlife

We introduce a simple recurrent variational auto-encoder architecture that
significantly improves image modeling. The system represents the
state-of-the-art in latent variable models for both the ImageNet and Omniglot
datasets. We show that it naturally separates global conceptual information
from lower level details, thus addressing one of the fundamentally desired
properties of unsupervised learning. Furthermore, the possibility of
restricting ourselves to storing only global information about an image allows
us to achieve high quality 'conceptual compression'.

通过引入一种简单的循环变分自编码器结构，我们在图像建模方面取得了显著进展，实现了割裂了关于全局概念信息和底层细节的分离，达到了无监督学习的基石之一。同时，我们通过仅存储关于图像的全局信息，实现了高质量的 ' 概念压缩 '。