# Enhancing Sign Language Recognition Performance Through Coverage-Based Dynamic Clip Generation
## 2 Related Works  
In this section, we review relevant research on efficient video processing for sign language recognition and related domains. We categorize the existing works into three main themes: frame and resolution optimization, adaptive sampling and keypoint detection, and multimodal and hybrid approaches. Table 1 summarizes key research focused on frame and resolution optimization techniques. [PERSON] et al. proposed a method that reduces computational cost while maintaining recognition accuracy by selecting only the most informative video segments. A lightweight network extracts coarse features, and a policy network dynamically selects key subsequences for processing, thereby reducing redundant computation. Furthermore, the adaptive model AdaBrowse improves efficiency by adjusting the input resolution based on spatial redundancy, achieving state-of-the-art performance with lower FLOPs [13]. For continuous sign language recognition, [PERSON] et al. also introduced a scalable frame resolution approach that dynamically adjusts resolution based on task context. By lowering the resolution when high detail is unnecessary, the method improves processing speed without compromising accuracy, making real-time CSLR systems more efficient [14]. [PERSON] et al. proposed a time-efficient sign language recognition approach by combining key frame extraction with temporal shift modeling. Their method uses the inter-frame difference technique to identify motion-salient frames, thereby avoiding the need to process full-length videos. The selected frames are then passed through a ResNet50 backbone integrated with the Temporal Shift Module (TSM), allowing for temporal modeling across frames with minimal additional computation. This approach achieves state-of-the-art accuracy on Chinese Sign Language datasets while significantly reducing the number of processed frames and parameters [15]. [PERSON] et al. proposed a novel framework called STNet, which enhances spatio-temporal feature representation through frame-level optimization techniques. Their method introduces a Spatial Resonance Module (SRM) that employs the Sinkhorn-Knopp algorithm to model feature transport between adjacent frames, thereby improving the continuity and consistency of extracted features. To preserve frame-specific details, they also propose a frame-wise loss function that enhances feature discriminability across frames. Furthermore, the Multi-Temporal Perception Module (MTPM) aggregates features at multiple temporal scales, enabling effective modeling of both short- and long-term dependencies. STNet demonstrates competitive performance across multiple CSLR benchmarks while maintaining high efficiency through reduced frame redundancy [16]. Table 2 summarizes the research focused on adaptive sampling and keypoint detection techniques. \begin{table}
\begin{tabular}{l p{142.3 pt}} \hline \hline
**Researcher** & **Key Techniques** \\ \hline \multirow{3}{*}{[PERSON] et al. [13]} & AdaBrowse: Selectively processes only the most informative parts of the video, reducing computational cost while maintaining recognition accuracy \\ \cline{2-3}  & Dynamic Resolution Adjustment: Dynamically adjusts input frame resolution to reduce computational cost and time \\ \hline \multirow{3}{*}{[PERSON] et al. [15]} & Key Frame Extraction + TSM: Selects frames based on inter-frame difference and uses time-shift modules to model temporal features with reduced overhead \\ \cline{2-3}  & STNet: Enhances temporal consistency and frame-wise distinctiveness using optimal transport, frame-wise loss, and multi-scale temporal fusion \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Frame and resolution optimization techniques. [PERSON] et al. introduced a novel keypoint-based approach for detecting the start and end points of sign language gestures. Their method extracts keypoint information from signers and monitors changes in these keypoints over time to identify gesture boundaries.