saliency-2016-cvpr:浅层和深层卷积网络用于显着性预测

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浅层和深层卷积网络用于显着性预测 该论文在 (*) (*) (*)平等贡献 之间的联合合作: 抽象的 传统上,基于神经科学原理通过手工制作的功能解决了图像显着区域的预测问题。 但是,本文通过训练卷积神经网络(convnet),以完全数据驱动的方法解决了该问题。 学习过程被表述为损失函数的最小化,该损失函数使用提供的地面真实性来测量预测显着性图的欧几里得距离。 最近发布的显着性预测大型数据集提供了足够的数据来训练快速而准确的端到端体系结构。 提出了两种设计:从头开始训练的浅层卷积网络,以及另一种更深层次的解决方案,其前三层改编自另一种经过训练的分类网络。 据作者所知,这是为显着性预测目的而经过培训和测试的首批端到端CNN 出版物 感谢计算机科学基金会的支持, 得以公开发表。 也可以使用。 请引用以下Bibtex代码: @InProceedings{Pan_2016_CVPR, au

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