불균형 블랙박스 동영상 데이터에서 충돌 상황의 다중 분류를 위한 손실 함수 비교 = Comparison of Loss Function for Multi-Class Classification of Collision Events in Imbalanced Black-Box Video Data

다국어 초록 (Multilingual Abstract)

Data imbalance is a common issue encountered in classification problems, stemming from a significant disparity in the number of samples between classes within the dataset. Such data imbalance typically leads to problems in classification models, including overfitting, underfitting, and misinterpretation of performance metrics. Methods to address this issue include resampling, augmentation, regularization techniques, and adjustment of loss functions. In this paper, we focus on loss function adjustment, particularly comparing the performance of various configurations of loss functions (Cross Entropy, Balanced Cross Entropy, two settings of Focal Loss: α = 1 and α = Balanced, Asymmetric Loss) on Multi-Class black-box video data with imbalance issues. The comparison is conducted using the I3D, and R3D_18 models.