论文标题
统计注意力定位（SAL）：方法和对象分类的应用
Statistical Attention Localization (SAL): Methodology and Application to Object Classification
论文作者
论文摘要
提出了一种统计注意力定位（SAL）方法，以促进本工作中的对象分类任务。 SAL由三个步骤组成：1）通过决策统计数据的初步注意窗口选择，2）注意力图的细化和3）矩形注意区域的最终确定。 SAL计算本地平方窗的软确定得分，并使用它们在步骤1中识别明显区域。为了适应各种尺寸和形状的对象，SAL会在步骤2中优化初步结果，并获得更灵活形状的注意力图。最后，SAL在使用IS ANSPAINPE中的iS Anpection a Anpection a Anpection a Anpection a Anpection a Anpection a Anpection a Anpection a e e e-ixixel a Anpection a Anpection。学习（SSL），作为基线。我们应用SAL以获取裁剪和调整大小的注意区域作为替代输入。整个图像的分类结果以及注意区域都被结合起来，以达到最高的分类精度。给出了CIFAR-10数据集上的实验，以证明SAL辅助对象分类方法的优势。
A statistical attention localization (SAL) method is proposed to facilitate the object classification task in this work. SAL consists of three steps: 1) preliminary attention window selection via decision statistics, 2) attention map refinement, and 3) rectangular attention region finalization. SAL computes soft-decision scores of local squared windows and uses them to identify salient regions in Step 1. To accommodate object of various sizes and shapes, SAL refines the preliminary result and obtain an attention map of more flexible shape in Step 2. Finally, SAL yields a rectangular attention region using the refined attention map and bounding box regularization in Step 3. As an application, we adopt E-PixelHop, which is an object classification solution based on successive subspace learning (SSL), as the baseline. We apply SAL so as to obtain a cropped-out and resized attention region as an alternative input. Classification results of the whole image as well as the attention region are ensembled to achieve the highest classification accuracy. Experiments on the CIFAR-10 dataset are given to demonstrate the advantage of the SAL-assisted object classification method.
