OTAS: Unsupervised Boundary Detection for Object-Centric Temporal Action Segmentation

TL;DR

OTAS introduces an unsupervised, object-centric approach for temporal action segmentation that leverages local features, outperforming state-of-the-art methods and even surpassing human annotations in some cases.

Contribution

The paper presents OTAS, a novel unsupervised framework combining global and local features for boundary detection in action segmentation, with a comprehensive evaluation of metrics.

Findings

OTAS outperforms previous methods by 41% in F1 score.

OTAS surpasses ground-truth annotations in user studies.

OTAS enables real-time inference.

Abstract

Temporal action segmentation is typically achieved by discovering the dramatic variances in global visual descriptors. In this paper, we explore the merits of local features by proposing the unsupervised framework of Object-centric Temporal Action Segmentation (OTAS). Broadly speaking, OTAS consists of self-supervised global and local feature extraction modules as well as a boundary selection module that fuses the features and detects salient boundaries for action segmentation. As a second contribution, we discuss the pros and cons of existing frame-level and boundary-level evaluation metrics. Through extensive experiments, we find OTAS is superior to the previous state-of-the-art method by $41\%$ on average in terms of our recommended F1 score. Surprisingly, OTAS even outperforms the ground-truth human annotations in the user study. Moreover, OTAS is efficient enough to allow real-time inference.