# Multi-Grained Temporal Clip Transformer for Skeleton-Based Human Activity Recognition
## 4 Experiments
### Ablation Studies
#### 4.3.6 Visualizations  
To evaluate the effectiveness of MTC-Former, we visualize and analyze the ICSA outputs at different granularity levels. Figure 11 presents two representative activities (\"put on glasses\" and \"reading\") for comparative analysis. The horizontal and vertical axes indicate the joint and frame indices, respectively. In the activation map, the darker the color, the more critical the frame is to the short-term motion.  
An analysis of the two distinct behaviors reveals that the feature activation for \"put on glasses\" is primarily concentrated in the mid-temporal region, while for the activity \"reading\", the activation is predominantly localized in the early-temporal region. This pattern aligns with the temporal dynamics of the respective actions, indicating that the proposed method effectively focuses on skeleton frames highly relevant to action-specific categorization, while filtering irrelevant information, consequently enhancing human activity recognition (HAR) performance.  
Our analysis reveals that different ICSA modules focus on distinct temporal regions, capturing short-term motion features at varying granularity levels. For \"put on glasses\", the output feature activations of ICSA (\(n=1\)) and ICSA (\(n=2\)) are mainly concentrated between frames 25 and 35, corresponding to the middle motion phase. In contrast, ICSA (\(n=3\)) focuses on the range of 31 to 35 frames, capturing the motion's critical phase. This observation demonstrates that the proposed ICSA modules effectively capture short-term motion information at different granularities.  
We employed the confusion matrix to evaluate the confusion levels among different categories of human activities. As illustrated in Figure 12a, most activity categories achieve high classification accuracy. Notably, as shown in Figure 12b, action categories 95-100, which require long-term temporal modeling, exhibit high classification accuracy. This indicates that our model can effectively capture long-term motion features. However, as depicted in Figure 12c, the accuracy is relatively lower for certain behaviors within  
Figure 11: The attention maps of the output features of ICSA module.  
categories 71 to 76. In activity categories involving significant limb movements, such as arm swings and kicks, our method achieves excellent recognition accuracy. However, activities involving subtle movements, such as \"making an OK sign\" or \"make a victory sign\", exhibit lower recognition accuracy. Additionally, activities involving interaction with small tools, such as \"cutting nails or paper\", also exhibit lower recognition rates. Specifically, the 73 rd activity, \"staple book\", exhibits the lowest recognition accuracy and is frequently misclassified as the 76 th activity, \"cutting paper\", as skeleton data lack object interaction details. Similarly, the 71 st activity, \"making an OK sign\", and the 72 nd activity, \"making a victory gesture\", show lower accuracy and are often misclassified as each other. The critical distinction resides in subtle finger posture variations, which are challenging to capture owing to the constrained number of hand joints.