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Multi-task image classification via collaborative, hierarchical spike-and-slab priors

机译:通过协作,分层峰值和平板电视机的多任务图像分类

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Promising results have been achieved in image classification problems by exploiting the discriminative power of sparse representations for classification (SRC). Recently, it has been shown that the use of class-specific spike-and-slab priors in conjunction with the class-specific dictionaries from SRC is particularly effective in low training scenarios. As a logical extension, we build on this framework for multitask scenarios, wherein multiple representations of the same physical phenomena are available. We experimentally demonstrate the benefits of mining joint information from different camera views for multi-view face recognition.
机译:通过利用分类(SRC)稀疏表示的辨别力来实现了有希望的结果。 最近,已经表明,在低训练场景中,使用与SRC的特定类词典结合使用类别特定的峰值电视机。 作为逻辑扩展,我们在该框架上构建用于多任务方案的框架,其中可以使用相同物理现象的多个表示。 我们通过实验展示了来自不同相机视图的挖掘联合信息的益处,以进行多视图人脸识别。

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