UBoCo : Unsupervised Boundary Contrastive Learning for Generic Event Boundary Detection

TL;DR

This paper introduces a novel unsupervised and supervised framework for generic event boundary detection in videos, leveraging Temporal Self-similarity Matrices and a new parsing algorithm to achieve state-of-the-art results, even surpassing some supervised methods.

Contribution

It proposes a new framework using TSM and a recursive parsing algorithm combined with contrastive learning, significantly improving GEBD performance in both unsupervised and supervised settings.

Findings

Unsupervised method outperforms previous supervised models.

State-of-the-art performance achieved on GEBD benchmarks.

Framework applicable to both unsupervised and supervised scenarios.

Abstract

Generic Event Boundary Detection (GEBD) is a newly suggested video understanding task that aims to find one level deeper semantic boundaries of events. Bridging the gap between natural human perception and video understanding, it has various potential applications, including interpretable and semantically valid video parsing. Still at an early development stage, existing GEBD solvers are simple extensions of relevant video understanding tasks, disregarding GEBD's distinctive characteristics. In this paper, we propose a novel framework for unsupervised/supervised GEBD, by using the Temporal Self-similarity Matrix (TSM) as the video representation. The new Recursive TSM Parsing (RTP) algorithm exploits local diagonal patterns in TSM to detect boundaries, and it is combined with the Boundary Contrastive (BoCo) loss to train our encoder to generate more informative TSMs. Our framework can be applied to both unsupervised and supervised settings, with both achieving state-of-the-art performance by a huge margin in GEBD benchmark. Especially, our unsupervised method outperforms the previous state-of-the-art "supervised" model, implying its exceptional efficacy.