# Improving Time Study Methods Using Deep Learning-Based Action Segmentation Models
## 7 Discussion  
**Viewpoints Impact:** Utilizing different viewpoints, specifically Overhead (OH) and Hand-Focused (HF), has been shown to influence model performance. The OH viewpoint provides a comprehensive perspective, capturing large-scale activities, but it may miss finer details critical for complex tasks. Conversely, the HF viewpoint excels in capturing detailed hand movements but may lose sight of the broader context. Combining OH and HF (OH + HF) aims to merge these strengths, leading to a more balanced and comprehensive understanding of activities. This integrated approach improves the F1@IoU scores, as demonstrated by the results in Table 4, where the three best models use this approach, indicating enhanced segmentation precision by incorporating a variety of visual cues. **Feature Extraction Methods:** The results outlined in Table 4 reveal that models using the FE method for feature extraction displayed higher frame-wise accuracy but lower F1@IoU scores. This suggests that, while these models accurately classify individual frames, they struggle with precise action boundary delineation. The general features learned from extensive datasets like ImageNet may not provide the specificity needed for accurate temporal segmentation in our dataset. In contrast, the FT method showed significant improvements in F1@IoU scores, reflecting the success of adapting pretrained features to our dataset's unique characteristics. However, the benefits of the TN method, which involves training a model from scratch on our dataset, are not as clearly defined. The impact on the F1@IoU metric is not a marked improvement over the FT method, especially considering the resource-intensive nature of training a new model, suggesting a delicate balance between dataset specificity and pretrained features. **Action segmentation model type:** The Transformer (TFORM) models, designed with an emphasis on the encoder architecture and devoid of dilated temporal convolutional layers, were expected to leverage the global attention mechanism for capturing broad temporal relationships. While their global attention mechanism effectively captured broader temporal relationships, as indicated by their accuracy scores, they fell short in F1@IoU performance. This suggests that the global attention mechanism, while powerful, may not be sufficient for the fine-grained segmentation required in complex action segmentation tasks. The number of parameters in these models, while substantial, did not directly correlate with superior F1@IoU performance, suggesting that architectural choices play a more significant role than model size in this context. A potential reason for this could be the inherent lack of inductive bias in the Transformer architecture, which, when combined with a relatively smaller dataset, could impede the model's ability to learn fine-grained temporal dynamics essential for high F1@IoU scores. Furthermore, the 1D CNN and biLSTM models demonstrated a commendable balance between accuracy and F1@IoU scores. These architectures' ability to efficiently handle temporal data, despite having fewer parameters than some Transformer models, underscores the importance of architectural suitability over sheer model size for specific tasks like action segmentation. **Segmentation challenges:** By observing the qualitative results in Figure 13, specifically in the column \"Worst segmentation results\", it is evident that all model types face challenges in distinguishing background activities (labeled as light yellow) from other actions. The difficulty with background activities could stem from their diverse nature and under-representation in the dataset. The inconsistency in appearance makes it challenging for models to establish stable prediction rules, leading to lower F1@IoU scores in the worst-case scenarios. Conversely, the best-case scenarios showcase instances where background activities are minimal, or the models have learned to segment actions with high precision due to specific contextual features present in those examples.