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- W4316660381 abstract "Tire pattern image classification is an important computer vision problem in pubic security, which can guide policeman to detect criminal cases. It remains challenge due to the small diversity within different classes. Generally, a tire pattern image classification system may require two characteristics: high accuracy and low computation. In this paper, we first assume that capturing rich feature representation will benefits tire classification and learning through a lightweight network will improve computing efficiency. We then propose a simple yet efficient two-stage training mechanism: 1) We learn a feature extractor using a V ariational Auto-Encoder framework constrained by contrastive learning, projecting images to latent space owing rich feature representation. 2) We train a single-layer linear classification network depend on the features extracted by the previous trained encoder. The Top-1 and Top-5 accuracy on tire pattern dataset is 89.8% and 96.6% respectively, validating the effectiveness of our strategy." @default.
- W4316660381 created "2023-01-17" @default.
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- W4316660381 date "2022-12-13" @default.
- W4316660381 modified "2023-10-18" @default.
- W4316660381 title "Tire Pattern Image Classification using Variational Auto-Encoder with Contrastive Learning" @default.
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- W4316660381 doi "https://doi.org/10.1109/vcip56404.2022.10008835" @default.
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