# Enhancing Sign Language Recognition Performance Through Coverage-Based Dynamic Clip Generation
## 2 Related Works
This approach significantly improves recognition accuracy while maintaining computational efficiency, achieving state-of-the-art performance on multiple CSLR datasets [22]. [PERSON] et al. proposed a novel method for continuous sign language recognition by combining object detection with sequence encoding. The YOLOX model, equipped with a Dual-branch Shuffle Attention (DSA) mechanism, is used to detect head and hand positions, while the CloU loss function enhances performance under occlusion. Sign language videos are encoded as 1D sequences, and the combination of FastDTW and BiLSTM is employed to improve recognition accuracy. This approach reduces the Word Error Rate (WER) by 21.26% compared to DTW-HMM and by 11.53% compared to LSTM-A, while also lowering computational complexity, offering a faster and more accurate solution [23]. [PERSON] et al. proposed a weighted multi-modal sign language recognition framework, WMSLRnet, which systematically balances the contributions of multiple input modalities to improve classification accuracy. Rather than assigning equal weights or relying on heuristics, WMSLRnet employs a grid search strategy to determine the optimal weights for RGB, hand crop, and skeletal pose modalities. Each modality is processed by a dedicated Channel-Separated Convolutional Network (CSN) backbone, and the final prediction is generated through a weighted sum of the modality-specific outputs. The authors demonstrated that even small changes in modality weights can significantly impact recognition performance. Experiments conducted on the WLASL100 dataset show that WMSLRnet achieves a Top-1 accuracy of 84.5%, outperforming several existing multi-modal and transformer-based models. Notably, this performance is achieved without the need for extensive pre-training or additional modalities such as depth or optical flow, making WMSLRnet a simple yet highly effective solution for sign language recognition [24]. While conventional methods typically generate a fixed number of clips regardless of video length, our proposed method dynamically adjusts the number of clips based on each video's length using a novel metric called coverage. This metric evaluates how effectively the generated clips represent the entire video and allows for frame reuse when appropriate. As a result, the method produces more clips for longer videos to capture sufficient temporal context, and fewer clips for shorter videos to reduce unnecessary \begin{table}
\begin{tabular}{l l} \hline
**Researcher** & **Key Techniques** \\ \hline \multirow{4}{*}{[PERSON] et al. [22]} & Cross-Modal Adaptive Prototype Learning (CAP-SLR): Integrates keyframe extraction, multi-scale dilated convolutional attention, and adaptive prototype updating to enhance sign language recognition accuracy \\ \hline \multirow{4}{*}{[PERSON] et al. [23]} & Continuous Sign Language Recognition: Integrates object detection (YOLOX) with Dual-branch Shuffle Attention Mechanism (DSA) and a 1D sequence encoding approach to reduce WER and computational complexity \\ \hline \multirow{4}{*}{[PERSON] et al. [24]} & Weighted Multi-modal Sign Language Recognition (WMSLRnet): Combines RGB, hand crop, and skeletal pose modalities using 3D CNN (CSN) backbones with optimized modality weights through grid search to improve classification accuracy without additional pre-training \\ \cline{1-1}  & \\ \cline{1-1}  & \\ \end{tabular}
\end{table} 
Table 3: Multimodal and hybrid approaches. computation. This dynamic method improves inference efficiency without compromising recognition performance.