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Abstract

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.