用半监督学习提升交通事件检测，低标签率下仍表现优异。
FPMT: Enhanced Semi-Supervised Model for Traffic Incident Detection
- 基于MixText框架，引入生成对抗网络增强数据并设计概率伪混合机制。
- 在四个真实数据集上表现优异，标签率极低时仍保持稳定性能。
- 适合标注成本高的交通监控场景，尤其适用于标签稀缺的部署环境。
交通事件检测的数据与标注获取成本高昂，使得半监督学习成为极具挑战性且重要的研究方向。本文提出一种名为FPMT的半监督学习模型，基于MixText框架构建。数据增强模块引入生成对抗网络以平衡和扩展数据集；在隐藏空间的mix-up过程中，采用概率伪混合机制增强正则化并提升模型精度。训练策略上，先对全部数据进行无监督训练，再在部分标注数据上进行有监督微调，最终完成半监督训练目标。在四个真实数据集上的实证验证表明，该模型在多种指标上均表现优异，尤其在低标签率场景下展现出强鲁棒性。
原文摘要 · Abstract (English)
For traffic incident detection, the acquisition of data and labels is notably resource-intensive, rendering semi-supervised traffic incident detection both a formidable and consequential challenge. Thus, this paper focuses on traffic incident detection with a semi-supervised learning way. It proposes a semi-supervised learning model named FPMT within the framework of MixText. The data augmentation module introduces Generative Adversarial Networks to balance and expand the dataset. During the mix-up process in the hidden space, it employs a probabilistic pseudo-mixing mechanism to enhance regularization and elevate model precision. In terms of training strategy, it initiates with unsupervised training on all data, followed by supervised fine-tuning on a subset of labeled data, and ultimately completing the goal of semi-supervised training. Through empirical validation on four authentic datasets, our FPMT model exhibits outstanding performance across various metrics. Particularly noteworthy is its robust performance even in scenarios with low label rates.
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