# Cross-Modality Interaction-Based Traffic Accident Classification
## 5 Discussion
### Comparing Self-Attention and Cross-Attention  
Moment-DETR [12] and QD-DETR [14] differ in how they process video and text features. In the Moment-DETR, the features of the video and text are simply concatenated to form the query, key, and value in the transformer encoder's self-attention mechanism. In the QD-DETR, within the cross-attention transformer, the video features are defined as the query, while the text features are the key and value. The attention mechanism takes the dot-product of the query and key matrices to produce an attention score, which is then the dot-product with the value. The attention score is calculated by taking the dot-product of the query with the key, allowing only those with high similarity to prevail. In video highlight detection, the relevance between video and text is crucial. Here, it is demonstrated which of the two models' methods is more efficient in processing video and text features.  
In Table 2, Moment-DETR CS exhibits the most outstanding performance, achieving 78.75% in [EMAIL_ADDRESS] and 57.50% in MR-Accuracy. In Table 3, Moment-DETR CMachieves 81.91% in [EMAIL_ADDRESS] and 63.83% in MR-Accuracy, which is slightly higher than QD-DETR CM by 1.06% in [EMAIL_ADDRESS] and by 2.13% in MR-Accuracy. In Table 4, Moment-DETR CM shows 87.14% in [EMAIL_ADDRESS] and 74.29% in MR-Accuracy, surpassing QD-DETR CM by 10% in [EMAIL_ADDRESS] and by 20% in MR-Accuracy.  
In Table 2, Moment-DETR slightly outperforms QD-DETR in the moment retrieval evaluation metrics. Additionally, it can be observed that in Tables 3 and 4, Moment-DETR performs better than QD-DETR. In a dataset characterized by the absence of audio and relatively short video lengths, a structure that simply concatenates video and text has been shown to surpass the performance of a structure that utilizes cross-attention between the video and text in the detection of traffic accident segments.  
In Table 5, QD-DETR CS shows the best performance with 70.17% in [EMAIL_ADDRESS] and Moment-DETR CS leads in MR-accuracy with 74.47%. In Table 6, Moment-DETR CM records 72.47% in [EMAIL_ADDRESS] and 75.55% in MR-accuracy, outperforming QD-DETR CM by 6.85% in [EMAIL_ADDRESS] and by 5.64% in MR-accuracy. Moreover, in Table 7, QD-DETR CR scores 67.38% in [EMAIL_ADDRESS] and 71.93% in MR-accuracy, which is higher than Moment-DETR CR by 3.09% in [EMAIL_ADDRESS] and by 3.51% in MR-accuracy.  
In Table 5, Moment-DETR CS and QD-DETR CS exhibit similar performance in the moment retrieval evaluation metrics. In Tables 6 and 7, the \"Cars collide\" accident segment detection performance is best with Moment-DETR CM, while the \"Accident\" accident segment detection performance is best with QD-DETR CR. In Tables 5-7, CCHD shows that the performance differences among the models in the moment retrieval metrics are not considerable. Our study demonstrates that the performance of traffic accident detection models varies depending on the query dataset and the feature extraction method used, and we present the optimal traffic accident detection model for different types of accidents based on the feature extraction method.