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1. KDText: A Lightweight Scene Text Detector with Decoupling-Based Knowledge Distillation | |||
Lei Siyue,Yan DanFeng | |||
Computer Science and Technology 28 February 2023 | |||
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Abstract:In recent years, scene text detection task has made great progress. However, most of works are devoted to improving the performance of detector and pay little attention to the practical applications. In this paper, we propose to exploit a mask branch to detect arbitrary-shaped text accurately, and compress the model to reduce computation and storage costs, achieving a balance between speed and accuracy. Specifically, we distill intermediate features and text proposal classification to transfer dark knowledge to student text detector. In the distillation process, we treat textual and Background features differently and decouple positive and negative text proposals. Experimental results on the ICDAR 2015 and ICDAR 2017 MLT datasets demonstrate the superiority of our lightweight scene text detector. | |||
TO cite this article:Lei Siyue,Yan DanFeng. KDText: A Lightweight Scene Text Detector with Decoupling-Based Knowledge Distillation[OL].[28 February 2023] http://en.paper.edu.cn/en_releasepaper/content/4759241 |
2. A Hybrid Ant Colony Optimization for Continuous Domains | |||
XIAO Jing,LI LiangPing | |||
Computer Science and Technology 11 July 2011 | |||
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Abstract:Research on optimization in continuous domains gains much of focus in swarm computation recently. A hybrid ant colony optimization approach which combines with the continuous population-based incremental learning and the differential evolution for continuous domains is proposed in this paper. It utilizes the ant population distribution and combines the continuous population-based incremental learning to dynamically generate the Gaussian probability density functions during evolution. To alleviate the less diversity problem in traditional population-based ant colony algorithms, differential evolution is employed to calculate Gaussian mean values for the next generation in the proposed method. Experimental results on a large set of test functions show that the new approach is promising and performs better than most of the state-of-the-art ACO algorithms do in continuous domains. | |||
TO cite this article:XIAO Jing,LI LiangPing. A Hybrid Ant Colony Optimization for Continuous Domains[J]. |
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