Topic Detection in Continuous Sign Language Videos
This work addresses a gap in sign language understanding by extending analysis to longer, more diverse videos, though it is incremental as it builds on existing datasets and methods.
The paper tackles the problem of topic detection in continuous sign language videos, introducing a novel task and providing strong baselines using the large-scale How2Sign dataset.
Significant progress has been made recently on challenging tasks in automatic sign language understanding, such as sign language recognition, translation and production. However, these works have focused on datasets with relatively few samples, short recordings and limited vocabulary and signing space. In this work, we introduce the novel task of sign language topic detection. We base our experiments on How2Sign, a large-scale video dataset spanning multiple semantic domains. We provide strong baselines for the task of topic detection and present a comparison between different visual features commonly used in the domain of sign language.