Abstract
Sign language recognition systems are usually modeled as classification systems that map gesture videos to pre-defined glosses.But these systems do not allow similarity searches, where a user can search for similar gestures without knowing the corresponding gloss.This paper presents a pose-based video-to-video search framework for isolated signs, which acts as a reverse gesture dictionary.The system employs keypoints on the skeletal structure instead of RGB images.Two architectures are proposed for modeling temporal information: an encoder with self-attention in a Transformer architecture and a Spatial-Temporal Graph Convolutional Network (ST-GCN).The embedding space is optimized using metric learning objectives, including supervised contrastive learning and ArcFace angular margin loss.The performance of the retrieval system is evaluated on the WLASL dataset using ranking metrics like Recall@K and mean Average Precision (mAP).Experiments reveal that the temporal modeling using the Transformer architecture is an improvement over the graph-based modeling approach in the low-shot learning scenario.The attention-based temporal pooling approach further enhances the ranking quality, with the best-performing model achieving an mAP of 0.237 on the WLASL validation set.Cross-dataset evaluation on a 226-label AUTSL dataset reveals non-trivial generalization performance on the unseen dataset, despite training only on the WLASL dataset.