Humans use multiple senses to comprehend the environment. Vision and language
are two of the most vital senses since they allow us to easily communicate our
thoughts and perceive the world around us. There has been a lot of interest in
creating video-language understanding systems with human-like senses since a
video-language pair can mimic both our linguistic medium and visual environment
with temporal dynamics. In this survey, we review the key tasks of these
systems and highlight the associated challenges. Based on the challenges, we
summarize their methods from model architecture, model training, and data
perspectives. We also conduct performance comparison among the methods, and
discuss promising directions for future research.

本文综述了视频 - 语言理解系统的关键任务、相关挑战以及模型架构、模型训练和数据观点等方面的方法，并进行了方法间的性能比较，提出了未来研究的有前景的方向。

视频语言理解：模型架构、模型训练和数据视角的综述

Video-Language Understanding: A Survey from Model Architecture, Model  Training, and Data Perspectives

Knowledge Tracing (KT) aims to predict the future performance of students by
tracking the development of their knowledge states. Despite all the recent
progress made in this field, the application of KT models in education systems
is still restricted from the data perspectives: 1) limited access to real life
data due to data protection concerns, 2) lack of diversity in public datasets,
3) noises in benchmark datasets such as duplicate records. To resolve these
problems, we simulated student data with three statistical strategies based on
public datasets and tested their performance on two KT baselines. While we
observe only minor performance improvement with additional synthetic data, our
work shows that using only synthetic data for training can lead to similar
performance as real data.

知识追踪旨在通过跟踪学生知识状态的发展来预测他们未来的表现。我们通过四个方面改进 KT 模型在教育系统中的应用：1）限制真实生活数据的获取，2）公共数据集的多样性不足，3）基准数据集中的噪音问题，4）利用模拟数据进行性能测试。尽管我们在额外的模拟数据上仅观察到轻微的性能提升，但我们的研究表明仅使用模拟数据进行训练可以获得与真实数据相似的性能。