Transferable Unintentional Action Localization With Language-Guided Intention Translation.

Abstract

Unintentional action localization (UAL) is a challenging task that requires reasoning about action intention clues to detect the temporal locations of unintentional action occurrences in real-world videos. Previous efforts usually treated this task as a dense binary classification problem and did not fully explore the relationships between intention clues and unintentional actions, resulting in unsatisfactory performance on open-set scenarios during inference. In this paper, we propose a Transferable Unintentional Action Localization framework by introducing language-guided intention translation, which explicitly formulates unintentional action localization as an open-set localization problem. Our framework constructs a transferable reasoning model guided by natural languages to translate the action intention of the entire video, which generates natural and powerful supervision signals for reconstructing complete action intention clues to address the problem of unintentional action localization. Based on the fact that a video with failure action is composed of intentional and unintentional parts connected by a transient action transition. Our transferable reasoning model employs a transformer architecture to transfer knowledge between intentional and unintentional parts for learning complementary semantic representations of these two parts, completing the action intention clue in an implicit supervision manner. We also present a dense voting scheme for detecting the action transition from intentional to unintentional using discriminative representations incorporating action intention clues. Extensive experiments demonstrate that our framework outperforms representative unintentional action localization methods in a wide range of open-set scenarios. In addition, we create a new unintentional sports video dataset, FS-Falls, and extend our framework from in-the-wild scenarios to competitive sports to demonstrate better generalization ability. We hope this work will provide a new perspective on creating powerful representations with complete action intention priors, which will help us better understand human action and capture underlying intention clues in real-world videos.