  Most of the vision-based sign language research to date has focused on
Isolated Sign Language Recognition (ISLR), where the objective is to predict a
single sign class given a short video clip. Although there has been significant
progress in ISLR, its real-life applications are limited. In this paper, we
focus on the challenging task of Sign Spotting instead, where the goal is to
simultaneously identify and localise signs in continuous co-articulated sign
videos. To address the limitations of current ISLR-based models, we propose a
hierarchical sign spotting approach which learns coarse-to-fine spatio-temporal
sign features to take advantage of representations at various temporal levels
and provide more precise sign localisation. Specifically, we develop
Hierarchical Sign I3D model (HS-I3D) which consists of a hierarchical network
head that is attached to the existing spatio-temporal I3D model to exploit
features at different layers of the network. We evaluate HS-I3D on the ChaLearn
2022 Sign Spotting Challenge - MSSL track and achieve a state-of-the-art 0.607
F1 score, which was the top-1 winning solution of the competition.
