###### Abstract
For temporal modeling [21], RNN-based models excel in understanding sign language sequences but struggle with parallelizing sequence processing and capturing long-distance contextual relationships due to their network structure [22; 23]. Although the Transformer model can address this issue [24], cSLR is a video-text multimodal task, where the information density and redundancy in the video sequence is much higher than that in the text data [25]. Therefore, the original self-attention mechanism cannot fully capture the contextual information and it is difficult to understand the semantics of sign language gestures, which leads to a weak recognition accuracy [18]. To address the above issues, we propose _MSF-ET_ (**M**ulti-**S**caled feature **F**usion **E**uclidean **T**ransformer), a novel framework for continuous sign language recognition. We improve both spatial feature extraction and temporal modeling to enhance the learning of high-level semantic information. Although multi-scale features are widely used in visual tasks, for SLR, it is more important to fully integrate visual information of different granularities to model sign language semantics. However, previous work has overlooked the research on feature fusion methods. We designed a multi-scaled feature fusion module to detect image details at different granularities in RGB images, as shown in Figure 1. Whereas existing work has previously employed the concept of multi-scale thinking to enhance continuous sign language translation [9], employing KNN to achieve multi-scaling along the temporal dimension, our approach diverges significantly. Our multi-scale focus emphasizes the incorporation of multi-granularity visual information. More critically, we introduce a multi-scale feature fusion method into cSLR. Although similar methods have been applied in other fields, the adaptation and integration of granularity-aware feature fusion and attention optimization to enhance cSLR present a distinct implementation tailored to the intricacies of sign language, which involves dynamic and nuanced gestures and expressions. Our spatial perception module outputs fused features, incorporating coarse-grained and fine-grained characteristics, enhancing the model's understanding of semantic information. Regarding the temporal dimension, we first analyze the problems of the original Transformer architecture in sign language video perception. We observe that the dot-product attention mechanism is susceptible to the influence of vector magnitudes when calculating attention weights. Building on this foundation, we refine the traditional Our main contributions are summarized as follows:  
* We introduce a methodology for fusing multi-scale visual feature maps, leveraging a self-attention mechanism to facilitate the flexible and dynamic integration of visual feature maps across diverse dimensions and granularities. * We analytically examine the limitations of the vanilla Transformer in modeling sign language videos and propose an improvement to the self-attention mechanism by substituting the dot product with Euclidean distance. * Extensive experiments and ablation studies validate the efficacy of our method, significantly enhancing the performance of Transformer-based models on the continuous cSLR task.