{"title":"FPMT:用于交通事故检测的增强型半监督模型","authors":"Xinying Lu, Jianli Xiao","doi":"arxiv-2409.07839","DOIUrl":null,"url":null,"abstract":"For traffic incident detection, the acquisition of data and labels is notably\nresource-intensive, rendering semi-supervised traffic incident detection both a\nformidable and consequential challenge. Thus, this paper focuses on traffic\nincident detection with a semi-supervised learning way. It proposes a\nsemi-supervised learning model named FPMT within the framework of MixText. The\ndata augmentation module introduces Generative Adversarial Networks to balance\nand expand the dataset. During the mix-up process in the hidden space, it\nemploys a probabilistic pseudo-mixing mechanism to enhance regularization and\nelevate model precision. In terms of training strategy, it initiates with\nunsupervised training on all data, followed by supervised fine-tuning on a\nsubset of labeled data, and ultimately completing the goal of semi-supervised\ntraining. Through empirical validation on four authentic datasets, our FPMT\nmodel exhibits outstanding performance across various metrics. Particularly\nnoteworthy is its robust performance even in scenarios with low label rates.","PeriodicalId":501301,"journal":{"name":"arXiv - CS - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"FPMT: Enhanced Semi-Supervised Model for Traffic Incident Detection\",\"authors\":\"Xinying Lu, Jianli Xiao\",\"doi\":\"arxiv-2409.07839\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For traffic incident detection, the acquisition of data and labels is notably\\nresource-intensive, rendering semi-supervised traffic incident detection both a\\nformidable and consequential challenge. Thus, this paper focuses on traffic\\nincident detection with a semi-supervised learning way. It proposes a\\nsemi-supervised learning model named FPMT within the framework of MixText. The\\ndata augmentation module introduces Generative Adversarial Networks to balance\\nand expand the dataset. During the mix-up process in the hidden space, it\\nemploys a probabilistic pseudo-mixing mechanism to enhance regularization and\\nelevate model precision. In terms of training strategy, it initiates with\\nunsupervised training on all data, followed by supervised fine-tuning on a\\nsubset of labeled data, and ultimately completing the goal of semi-supervised\\ntraining. Through empirical validation on four authentic datasets, our FPMT\\nmodel exhibits outstanding performance across various metrics. Particularly\\nnoteworthy is its robust performance even in scenarios with low label rates.\",\"PeriodicalId\":501301,\"journal\":{\"name\":\"arXiv - CS - Machine Learning\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Machine Learning\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.07839\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Machine Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.07839","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
FPMT: Enhanced Semi-Supervised Model for Traffic Incident Detection
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.