# Cross-Modality Interaction-Based Traffic Accident Classification
## 5 Discussion
### Feature Extraction  
The methods for extracting features from video and text play a crucial role in improving the performance of traffic accident detection. Notably, CLIP [21] is essential as it effectively combines visual and linguistic information by leveraging the interaction between images and the text.  
In Table 2, Moment-DETR CS achieves 78.75% in [EMAIL_ADDRESS] and 57.50% in MR-Accuracy, which is slightly higher, by 0.75% in [EMAIL_ADDRESS] and by 1.5% in MR-Accuracy, compared to Moment-DETR CM. Additionally, the difference between QD-DETR CS and CM is only 0.08% in [EMAIL_ADDRESS], while MR-Accuracy is identical at 56.50%.  
In Table 5, Moment-DETR CS scores 69.02% in [EMAIL_ADDRESS] and 74.47% in MR-Accuracy, which is 5.06% higher in [EMAIL_ADDRESS] and 3.46% higher in MR-Accuracy than Moment-DETR CR. Furthermore, In Table 5, among the three feature extraction methods, QD-DETR CS shows superior performance across all metrics. QD-DETR CS is higher by 4.86% in [EMAIL_ADDRESS], by 3.46% in MR-Accuracy, and by 1.14% in HL-Min-Fair-mAP than QD-DETR CM.  
In Table 8, among the three feature extraction methods, UMT and QD-DETR CS exhibit the most outstanding performance across all metrics. UMT CS scores 3.16% higher in [EMAIL_ADDRESS], 4.52% higher in HL-Min-Fair-mAP, and 2.13% higher in MR-Accuracy than UMT CR. Furthermore, QD-DETR CS outperforms QD-DETR CR by 0.79% in [EMAIL_ADDRESS], by 2.67% in HL-Min-Fair-mAP, and by 1.6% in MR-Accuracy.  
The combination of CLIP [21] and Slowfast [16] features demonstrates an overwhelmingly synergistic effect compared to when Resnet [17] features or MIL-NCE pre-trained S3D [18] features are combined with CLIP. CLIP interprets the semantics of images in a linguistic manner, while Slowfast recognizes semantic and temporal motions. By using both methods, we can obtain an integrated representation of language, objects, and motion in videos, and we have proven that this enhances the performance of traffic accident detection.  
In Table 2, among the three feature extraction methods, QD-DETR CM shows slightly higher performance across all evaluation metrics, with 78.25% in [EMAIL_ADDRESS], 49.08% in HL-Min-Fair-mAP, and 56.50% in MR-Accuracy. Additionally, in HL-Min-Fair-mAP, QD-DETR CM scores 9.08% higher than QD-DETR CS and 2.16% higher than QD-DETR CR.  
In CCHD with video lengths over 4 s, the feature extraction methods of CLIP and MIL-NCE pre-trained S3D do not exhibit superior performance across all evaluation metrics compared to other methods. The S3D network from MIL-NCE pre-trained S3D, which creates 3D convolutions from 2D convolutions, does not show high performance in datasets with relatively longer video lengths than the videos in DAD [19].  
In all experiments, the feature extraction methods using CLIP and 2D Resnet generally do not show superior performance compared to other methods, and while 2D Resnet extracts image information from videos, it loses temporal information due to the two-dimensional convolution [30]. Therefore, there is a limitation in that simply combining features extracted by CLIP and 2D Resnet does not adequately consider the temporal aspect.