Sign Language Video Retrieval with Free-Form Textual Queries
This work addresses the limited attention in searching sign language videos beyond keywords, offering a useful application for sign language technology users.
The paper tackles the problem of retrieving sign language videos using free-form textual queries by learning cross-modal embeddings on the How2Sign dataset, and proposes SPOT-ALIGN to improve sign video embeddings, resulting in enhanced performance in sign recognition and video retrieval tasks.
Systems that can efficiently search collections of sign language videos have been highlighted as a useful application of sign language technology. However, the problem of searching videos beyond individual keywords has received limited attention in the literature. To address this gap, in this work we introduce the task of sign language retrieval with free-form textual queries: given a written query (e.g., a sentence) and a large collection of sign language videos, the objective is to find the signing video in the collection that best matches the written query. We propose to tackle this task by learning cross-modal embeddings on the recently introduced large-scale How2Sign dataset of American Sign Language (ASL). We identify that a key bottleneck in the performance of the system is the quality of the sign video embedding which suffers from a scarcity of labeled training data. We, therefore, propose SPOT-ALIGN, a framework for interleaving iterative rounds of sign spotting and feature alignment to expand the scope and scale of available training data. We validate the effectiveness of SPOT-ALIGN for learning a robust sign video embedding through improvements in both sign recognition and the proposed video retrieval task.