Summarizing a video requires a diverse understanding of the video, ranging
from recognizing scenes to evaluating how much each frame is essential enough
to be selected as a summary. Self-supervised learning (SSL) is acknowledged for
its robustness and flexibility to multiple downstream tasks, but the video SSL
has not shown its value for dense understanding tasks like video summarization.
We claim an unsupervised autoencoder with sufficient self-supervised learning
does not need any extra downstream architecture design or fine-tuning weights
to be utilized as a video summarization model. The proposed method to evaluate
the importance score of each frame takes advantage of the reconstruction score
of the autoencoder's decoder. We evaluate the method in major unsupervised
video summarization benchmarks to show its effectiveness under various
experimental settings.

本文提出了一种基于自监督学习的无监督自编码器视频摘要方法，无需其他下游架构或微调权重即可实现摘要。该方法利用解码器的重构分数来评估每帧的重要性得分，并通过实验表明其在各种实验环境下均具有有效性。

无监督视频摘要的遮蔽自动编码器

Masked Autoencoder for Unsupervised Video Summarization

Human falls rarely occur; however, detecting falls is very important from the
health and safety perspective. Due to the rarity of falls, it is difficult to
employ supervised classification techniques to detect them. Moreover, in these
highly skewed situations it is also difficult to extract domain specific
features to identify falls. In this paper, we present a novel framework,
\textit{DeepFall}, which formulates the fall detection problem as an anomaly
detection problem. The \textit{DeepFall} framework presents the novel use of
deep spatio-temporal convolutional autoencoders to learn spatial and temporal
features from normal activities using non-invasive sensing modalities. We also
present a new anomaly scoring method that combines the reconstruction score of
frames across a temporal window to detect unseen falls. We tested the
\textit{DeepFall} framework on three publicly available datasets collected
through non-invasive sensing modalities, thermal camera and depth cameras and
show superior results in comparison to traditional autoencoder methods to
identify unseen falls.

本文提出了一个新的框架 ——DeepFall，将跌倒检测问题转化为异常检测问题，并利用深度时空卷积自编码器从常规活动中学习空间和时间特征，通过复合窗口中帧的重构分数来检测未知跌倒，应用于三个公开数据集时显示出了超越传统自编码器方法的优越性。