The paper provides a survey of the development of machine-learning techniques
for video analysis. The survey provides a summary of the most popular deep
learning methods used for human activity recognition. We discuss how popular
architectures perform on standard datasets and highlight the differences from
real-life datasets dominated by multiple activities performed by multiple
participants over long periods. For real-life datasets, we describe the use of
low-parameter models (with 200X or 1,000X fewer parameters) that are trained to
detect a single activity after the relevant objects have been successfully
detected. Our survey then turns to a summary of machine learning methods that
are specifically developed for working with a small number of labeled video
samples. Our goal here is to describe modern techniques that are specifically
designed so as to minimize the amount of ground truth that is needed for
training and testing video analysis systems. We provide summaries of the
development of self-supervised learning, semi-supervised learning, active
learning, and zero-shot learning for applications in video analysis. For each
method, we provide representative examples.

该研究论文通过调查机器学习技术在视频分析领域的发展情况，总结了用于人体活动识别的最流行的深度学习方法，其中涵盖了不同于由多个参与者在长时间内执行多个活动支配的现实数据集的标准数据集，介绍了在成功检测到相关对象后，通过训练仅用于检测单一活动的低参数模型（参数数量减少了 200X 或者 1,000X）的使用。然后，该研究论文总结了专门用于处理少量标记视频样本的机器学习方法，目的是描述现代技术，以减少用于训练和测试视频分析系统所需的基本真实数据量，该部分还包括无监督学习、半监督学习、主动学习和零样本学习在视频分析应用中的发展，以及每种方法的代表性例子。

应用于视频分析系统的机器学习方法综述

A Review of Machine Learning Methods Applied to Video Analysis Systems

Novel intent discovery automates the process of grouping similar messages
(questions) to identify previously unknown intents. However, current research
focuses on publicly available datasets which have only the question field and
significantly differ from real-life datasets. This paper proposes methods to
improve the intent discovery pipeline deployed in a large e-commerce platform.
We show the benefit of pre-training language models on in-domain data: both
self-supervised and with weak supervision. We also devise the best method to
utilize the conversational structure (i.e., question and answer) of real-life
datasets during fine-tuning for clustering tasks, which we call Conv. All our
methods combined to fully utilize real-life datasets give up to 33pp
performance boost over state-of-the-art Constrained Deep Adaptive Clustering
(CDAC) model for question only. By comparison CDAC model for the question data
only gives only up to 13pp performance boost over the naive baseline.

本文提出了一种用于大型电子商务平台的意图发现流程改进方法，该方法利用了领域内数据的自我监督和弱监督预训练语言模型，以及利用现实生活数据的对话结构进行微调的最佳方法，并使用所有这些方法结合使用，使真实生活数据的使用比仅使用问题数据的 CDAC 模型效果提高了多达 33pp。

超越研究数据集：工业场景下的新意图发现

Going beyond research datasets: Novel intent discovery in the industry  setting

Research on time-series similarity measures has emphasized the need for
elastic methods which align the indices of pairs of time series and a plethora
of non-parametric have been proposed for the task. On the other hand, deep
learning approaches are dominant in closely related domains, such as learning
image and text sentence similarity. In this paper, we propose
\textit{NeuralWarp}, a novel measure that models the alignment of time-series
indices in a deep representation space, by modeling a warping function as an
upper level neural network between deeply-encoded time series values.
Experimental results demonstrate that \textit{NeuralWarp} outperforms both
non-parametric and un-warped deep models on a range of diverse real-life
datasets.

该研究旨在提出一种新型的测量时间序列相似性的方法 ——NeuralWarp，通过建模深度表示空间中的时间序列索引的对齐模型，模拟乘法函数作为深层神经网络来建模。实验结果表明，相比于非参数方法和未调整的深层模型，NeuralWarp 在各种现实生活数据集上表现更好。