# Enhancing Sign Language Recognition Performance Through Coverage-Based Dynamic Clip Generation
## 2 Related Works
By applying a threshold-based technique to the average keypoint change, the system accurately determines when a sign begins and ends. Experimental results demonstrate that using ten keypoints enables precise boundary detection within approximately two frames, thereby improving the overall accuracy of sign language recognition. This approach underscores the importance of temporal keypoint analysis in enhancing recognition performance [17]. [PERSON] et al. proposed a dynamic sampling rate adjustment method that detects the boundaries of sign language utterances and adjusts the frame extraction rate accordingly, based on the number of sign language image frames that can be acquired per unit time. This dynamic approach improves the recognition accuracy across devices with varying computational capabilities, achieving up to 66.54% higher Top-1 accuracy and 83.64% higher Top-5 accuracy compared to the fixed sampling rate method [18]. [PERSON] et al. introduced the Dynamic Sampling Network (DSN), which optimally selects video clips during both training and inference using reinforcement learning. Unlike traditional fixed clip sampling methods, the DSN learns to focus on key segments, reducing computational cost while maintaining accuracy [19]  
[PERSON] et al. proposed the Kernel Temporal Segmentation (KTS), an adaptive frame sampling method that decomposes long videos into semantically consistent segments. By leveraging frame-to-frame similarity analysis, KTS outperforms uniform sampling, enhancing both video classification and temporal action localization performance [20]. [PERSON] et al. introduced a keypoint-guided spatial attention module to enhance visual feature extraction in continuous sign language recognition (CSLR). By leveraging pose keypoint heatmaps, their method dynamically focuses on informative regions such as the face and hands, thereby improving recognition accuracy while maintaining computational efficiency. This Spatial Attention Consistency (SAC) technique strengthens the model's ability to detect critical movements essential for sign language recognition [21]. Table 3 summarizes the research focused on multimodal and hybrid approaches. \begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline
**Researcher** & **Key Techniques** \\ \hline \multirow{4}{*}{[PERSON] et al. [17]} & Keypoint-Based Sign Language Start and End Point Detection: Extracts keypoint information, monitors changes over time, and applies thresholding to detect gesture boundaries with high accuracy \\ \hline \multirow{2}{*}{[PERSON] et al. [18]} & Dynamic Sampling Rate Adjustment: Dynamically adjusts the sampling rate based on the frame rate in real-time to improve recognition accuracy in a mobile environment \\ \hline \multirow{2}{*}{[PERSON] et al. [19]} & Dynamic Sampling Network: Dynamically selects important clips during both training and inference to reduce computational cost while maintaining performance \\ \hline \multirow{2}{*}{[PERSON] et al. [20]} & Kernel Temporal Segmentation: Divides videos into semantically consistent segments and samples frames from each segment \\ \hline \multirow{4}{*}{[PERSON] et al. [21]} & Keypoint-Guided Spatial Attention: Utilizes pose keypoint heatmaps to enhance the visual module’s focus on informative regions (face and hands), improving recognition accuracy in continuous sign language recognition (CSLR) \\ \hline \end{tabular}
\end{table} 
Table 2: Adaptive sampling and keypoint detection techniques. [PERSON] et al. introduced Cross-Modal Adaptive Prototype Learning (CAP-SLR) to enhance continuous sign language recognition by addressing temporal redundancy and semantic misalignment. CAP-SLR incorporates a Keyframe Extractor to reduce redundant frames, a Multi-Scale Dilated Convolutional Attention module to enhance spatial-temporal representations, and a Cross-Modal Adaptive Prototype Updating mechanism to dynamically align visual and textual features.