Summary of Fpmt: Enhanced Semi-supervised Model For Traffic Incident Detection, by Xinying Lu and Jianli Xiao
FPMT: Enhanced Semi-Supervised Model for Traffic Incident Detection
by Xinying Lu, Jianli Xiao
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the challenge of traffic incident detection using a semi-supervised learning approach. The proposed model, FPMT, within the MixText framework combines data augmentation and probabilistic pseudo-mixing to enhance regularization and precision. It starts with unsupervised training on all data, followed by supervised fine-tuning on labeled subsets, achieving outstanding performance on four authentic datasets across various metrics. Specifically, it performs robustly even in scenarios with low label rates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Traffic incident detection is a crucial challenge that requires collecting data and labels, which can be resource-intensive. This paper proposes a new approach using semi-supervised learning to detect traffic incidents. It creates a model called FPMT that uses a mix of unsupervised and supervised training to improve performance. The model works well on real datasets and even performs well when there are few labeled examples. |
Keywords
» Artificial intelligence » Data augmentation » Fine tuning » Precision » Regularization » Semi supervised » Supervised » Unsupervised
