Recent applications of machine learning in metal additive manufacturing (MAM)
have demonstrated significant potential in addressing critical barriers to the
widespread adoption of MAM technology. Recent research in this field emphasizes
the importance of utilizing melt pool signatures for real-time defect
prediction. While high-quality melt pool image data holds the promise of
enabling precise predictions, there has been limited exploration into the
utilization of cutting-edge spatiotemporal models that can harness the inherent
transient and sequential characteristics of the additive manufacturing process.
This research introduces and puts into practice some of the leading deep
spatiotemporal learning models that can be adapted for the classification of
melt pool image streams originating from various materials, systems, and
applications. Specifically, it investigates two-stream networks comprising
spatial and temporal streams, a recurrent spatial network, and a factorized 3D
convolutional neural network. The capacity of these models to generalize when
exposed to perturbations in melt pool image data is examined using data
perturbation techniques grounded in real-world process scenarios. The
implemented architectures demonstrate the ability to capture the spatiotemporal
features of melt pool image sequences. However, among these models, only the
Kinetics400 pre-trained SlowFast network, categorized as a two-stream network,
exhibits robust generalization capabilities in the presence of data
perturbations.

机器学习在金属增材制造中的应用研究表明，利用熔池特征进行实时缺陷预测是解决普及金属增材制造技术的关键障碍之一，本研究实践了基于新颖时空模型的深度学习，用于分类来自不同材料、系统和应用的熔池图像序列，并发现只有 Kinetics400 预训练的 SlowFast 双流网络能够在数据扰动下具备鲁棒的泛化能力。

利用熔池图像流评估用于印迹异常分类的关键时空学习器

Evaluation of Key Spatiotemporal Learners for Print Track Anomaly  Classification Using Melt Pool Image Streams

Computer vision has undergone a dramatic revolution in performance, driven in
large part through deep features trained on large-scale supervised datasets.
However, much of these improvements have focused on static image analysis;
video understanding has seen rather modest improvements. Even though new
datasets and spatiotemporal models have been proposed, simple frame-by-frame
classification methods often still remain competitive. We posit that current
video datasets are plagued with implicit biases over scene and object structure
that can dwarf variations in temporal structure. In this work, we build a video
dataset with fully observable and controllable object and scene bias, and which
truly requires spatiotemporal understanding in order to be solved. Our dataset,
named CATER, is rendered synthetically using a library of standard 3D objects,
and tests the ability to recognize compositions of object movements that
require long-term reasoning. In addition to being a challenging dataset, CATER
also provides a plethora of diagnostic tools to analyze modern spatiotemporal
video architectures by being completely observable and controllable. Using
CATER, we provide insights into some of the most recent state of the art deep
video architectures.

本文通过构建名称为 CATER 的视频数据集，从而提供了深入理解深度视频架构的诊断工具。在该数据集中，场景和对象的结构充满着隐含的偏见，这可以掩盖时间结构的变化，因此需要时空理解才能真正解决。

CATER：组合动作和时间推理诊断数据集

CATER: A diagnostic dataset for Compositional Actions and TEmporal  Reasoning

Basketball games evolve continuously in space and time as players constantly
interact with their teammates, the opposing team, and the ball. However,
current analyses of basketball outcomes rely on discretized summaries of the
game that reduce such interactions to tallies of points, assists, and similar
events. In this paper, we propose a framework for using optical player tracking
data to estimate, in real time, the expected number of points obtained by the
end of a possession. This quantity, called \textit{expected possession value}
(EPV), derives from a stochastic process model for the evolution of a
basketball possession; we model this process at multiple levels of resolution,
differentiating between continuous, infinitesimal movements of players, and
discrete events such as shot attempts and turnovers. Transition kernels are
estimated using hierarchical spatiotemporal models that share information
across players while remaining computationally tractable on very large data
sets. In addition to estimating EPV, these models reveal novel insights on
players' decision-making tendencies as a function of their spatial strategy.

本文提出了一种框架，利用运动员运动跟踪数据实时估计篮球进攻结束时实现预期得分数量，并通过分层时空模型估计转换核函数，以分享跨运动员的信息。