Learning A Repression Network For Precise Vehicle Search
摘要 公共安全领域视频监控数量爆炸性的增长将大规模车辆数据库的检索技术推到风口浪尖。精准车辆检索需要根据输入的查询图片查找出所有目标。车辆检索的难度在于相同视觉特征的车型之间可能十分相似。为了解决这个问题，文章提出了RepNet，一种多任务的网络结构来同时学习每个目标的大体特征与精细特征。此外受益于特征分类的高准确率，提出了一种bucket search方式来减少检索时间，并基本保持准确率。实
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相关文章
- 1. 车辆检索“LEARNING A REPRESSION NETWORK FOR PRECISE VEHICLE SEARCH”
- 2. 论文笔记：A Learning-based Iterative Method For Solving Vehicle Routing Problems(ICLR 2020)
- 3. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
- 4. 18-Learning-a-Discriminative-Feature-Network-for-Semantic-Segmentation
- 5. Learning a Discriminative Feature Network for Semantic Segmentation
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- 8. Learning Context Graph for Person Search
- 9. Search for a Range
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