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 improve the word error rate (WER) on PHOENIX14 by an absolute 1.6% compared to the previous SOTA.

In continuous sign language recognition (CSLR), 2D-CNN-based extractors are often insufficiently trained for spatial capture and struggle with temporal modeling. This leads to incomplete spatial discrimination, hindering the understanding actions across frames. To address these limitations, we propose Hierarchical Spatial-Temporal Enhancement network (HSTE) through two key modules: Cross-scale Semantic Alignment (CSA) and Temporal Extension Shift (TES). CSA innovatively utilizes multi-scale features generated within the network, enriching feature representation through semantic alignment across scales. By integrating a novel temporal shift strategy with dilated convolutions, TES expands the receptive field and captures temporal changes between frames. These modules work independently and are hierarchically integrated into the network in a plug-and-play manner. Extensive experiments show that our method achieves state-of-the-art performance on the challenging CSLR benchmarks: PHOENIX14, PHOENIX14-T, and CSL-Daily. Code will be available at https://github.com/justlis/HSTENet.

Current continuous sign language recognition (CSLR) methods typically rely on single or adjacent frames for calculations, which can overlook broader contextual information and result in lower accuracy. To address this issue, we introduce CVSign, which constructs an extended contextual space frame by frame while enabling comprehensive cross-frame interaction. Specifically, we present two innovative modules: Contextual Correspondence Awareness (CCA) and Contextual Variability Awareness (CVA). CCA enhances the relevance of contextual features by utilizing cross-frame multi-head query attention to identify and prioritize related areas while suppressing irrelevant regions. CVA captures motion changes at varying speeds by employing difference calculations between multiple frames, effectively minimizing static redundancy. Remarkably, experimental results show that CVSign outperforms the previous state-of-the-art method by a clear margin on widely used datasets, including PHOENIX14, PHOENIX14-T, and CSL-Daily.