The task of Generic Event Boundary Detection (GEBD) aims to detect moments in
videos that are naturally perceived by humans as generic and taxonomy-free
event boundaries. Modeling the dynamically evolving temporal and spatial
changes in a video makes GEBD a difficult problem to solve. Existing approaches
involve very complex and sophisticated pipelines in terms of architectural
design choices, hence creating a need for more straightforward and simplified
approaches. In this work, we address this issue by revisiting a simple and
effective self-supervised method and augment it with a differentiable motion
feature learning module to tackle the spatial and temporal diversities in the
GEBD task. We perform extensive experiments on the challenging Kinetics-GEBD
and TAPOS datasets to demonstrate the efficacy of the proposed approach
compared to the other self-supervised state-of-the-art methods. We also show
that this simple self-supervised approach learns motion features without any
explicit motion-specific pretext task.

该研究提出了一种简化的自监督方法并加入可区分的运动特征学习模块，用于识别视频中的 Generic Event Boundary Detection 问题。在 Kinetics-GEBD 和 TAPOS 数据集上的实验证明了该方法的可行性和有效性，并展示了该简单的自监督方法学习运动特征的无需显式的预文本任务。

面向通用事件边界检测的运动感知自我监督

Motion Aware Self-Supervision for Generic Event Boundary Detection

We propose novel dynamic multiscale graph neural networks (DMGNN) to predict
3D skeleton-based human motions. The core idea of DMGNN is to use a multiscale
graph to comprehensively model the internal relations of a human body for
motion feature learning. This multiscale graph is adaptive during training and
dynamic across network layers. Based on this graph, we propose a multiscale
graph computational unit (MGCU) to extract features at individual scales and
fuse features across scales. The entire model is action-category-agnostic and
follows an encoder-decoder framework. The encoder consists of a sequence of
MGCUs to learn motion features. The decoder uses a proposed graph-based gate
recurrent unit to generate future poses. Extensive experiments show that the
proposed DMGNN outperforms state-of-the-art methods in both short and long-term
predictions on the datasets of Human 3.6M and CMU Mocap. We further investigate
the learned multiscale graphs for the interpretability. The codes could be
downloaded from this https URL

该研究提出了一种新颖的动态多尺度图神经网络（DMGNN），以预测基于 3D 骨架的人体动作，整个模型采用编码器 - 解码器框架，并使用多尺度图计算单元（MGCU）提取不同尺度的特征并融合。实验证明，在 Human 3.6M 和 CMU MOCAP 数据集上，DMGNN 在短期和长期预测方面都优于现有的方法。