OLMD: Orientation-aware Long-term Motion Decoupling for Continuous Sign Language Recognition
Abstract
The primary challenge in continuous sign language recognition (CSLR) mainly stems from the presence of multi-orientational and long-term motions. However, current research overlooks these crucial aspects, significantly impacting accuracy. To tackle these issues, we propose a novel CSLR framework: Orientation-aware Long-term Motion Decoupling (OLMD), which efficiently aggregates long-term motions and decouples multi-orientational signals into easily interpretable components. Specifically, our innovative Long-term Motion Aggregation (LMA) module filters out static redundancy while adaptively capturing abundant features of long-term motions. We further enhance orientation awareness by decoupling complex movements into horizontal and vertical components, allowing for motion purification in both orientations. Additionally, two coupling mechanisms are proposed: stage and cross-stage coupling, which together enrich multi-scale features and improve the generalization capabilities of the model. Experimentally, OLMD shows SOTA performance on three large-scale datasets: PHOENIX14, PHOENIX14-T, and CSL-Daily. Notably, we improved the word error rate (WER) on PHOENIX14 by an absolute 1.6% compared to the previous SOTA
Keywords
Cite
@article{arxiv.2503.08205,
title = {OLMD: Orientation-aware Long-term Motion Decoupling for Continuous Sign Language Recognition},
author = {Yiheng Yu and Sheng Liu and Yuan Feng and Min Xu and Zhelun Jin and Xuhua Yang},
journal= {arXiv preprint arXiv:2503.08205},
year = {2025}
}