Unintentional actions are rare occurrences that are difficult to define
precisely and that are highly dependent on the temporal context of the action.
In this work, we explore such actions and seek to identify the points in videos
where the actions transition from intentional to unintentional. We propose a
multi-stage framework that exploits inherent biases such as motion speed,
motion direction, and order to recognize unintentional actions. To enhance
representations via self-supervised training for the task of unintentional
action recognition we propose temporal transformations, called Temporal
Transformations of Inherent Biases of Unintentional Actions (T2IBUA). The
multi-stage approach models the temporal information on both the level of
individual frames and full clips. These enhanced representations show strong
performance for unintentional action recognition tasks. We provide an extensive
ablation study of our framework and report results that significantly improve
over the state-of-the-art.

该研究提出了一种多阶段框架，利用固有偏差（如运动速度、运动方向和先后顺序）识别视频中意图转变为无意的点，并通过自监督训练进行了增强表示，以进行无意识行为识别任务。研究发现，该框架和表示方法对无意识行为的识别任务具有较强的性能。

利用自监督训练进行意外动作识别

Leveraging Self-Supervised Training for Unintentional Action Recognition

We introduce a novel self-supervised contrastive learning method to learn
representations from unlabelled videos. Existing approaches ignore the
specifics of input distortions, e.g., by learning invariance to temporal
transformations. Instead, we argue that video representation should preserve
video dynamics and reflect temporal manipulations of the input. Therefore, we
exploit novel constraints to build representations that are equivariant to
temporal transformations and better capture video dynamics. In our method,
relative temporal transformations between augmented clips of a video are
encoded in a vector and contrasted with other transformation vectors. To
support temporal equivariance learning, we additionally propose the
self-supervised classification of two clips of a video into 1. overlapping 2.
ordered, or 3. unordered. Our experiments show that time-equivariant
representations achieve state-of-the-art results in video retrieval and action
recognition benchmarks on UCF101, HMDB51, and Diving48.

介绍了一种新的自监督对比学习方法，旨在从未标记的视频中学习表示。该方法利用新的约束条件，以建立对时间变换等价的表示，并更好地捕捉视频动态。实验表明，时间等变表示在 UCF101，HMDB51 和 Diving48 的视频检索和动作识别基准测试中实现了最先进的结果。