论文标题
系统评估：细粒度的CNN与传统的CNN分类器
A Systematic Evaluation: Fine-Grained CNN vs. Traditional CNN Classifiers
论文作者
论文摘要
为了充分利用基本的分钟和微妙的差异，细粒分类器收集了有关类间变化的信息。由于同一类实体中的颜色，观点和结构之间的差异很小，因此该任务非常具有挑战性。由于观点与其他类别之间的差异与与众不同的差异之间的相似之处，分类变得更加困难。在这项工作中，我们调查了地标的General CNN分类器的性能，CNN分类器的性能在大规模分类数据集（在细粒度数据集上）呈现了一流的结果，并将其与最先进的细粒分类器进行了比较。在本文中，我们提出了两个具体的问题：（i）一般CNN分类器是否获得与细粒分类器相当的结果？ （ii）一般CNN分类器是否需要任何特定信息来改进细粒度的分类器？在整个工作中，我们在不引入特定于细粒数据集的任何方面的情况下训练一般CNN分类器。我们在六个数据集上展示了广泛的评估，以确定细粒分类器是否能够提高实验中的基线。
To make the best use of the underlying minute and subtle differences, fine-grained classifiers collect information about inter-class variations. The task is very challenging due to the small differences between the colors, viewpoint, and structure in the same class entities. The classification becomes more difficult due to the similarities between the differences in viewpoint with other classes and differences with its own. In this work, we investigate the performance of the landmark general CNN classifiers, which presented top-notch results on large scale classification datasets, on the fine-grained datasets, and compare it against state-of-the-art fine-grained classifiers. In this paper, we pose two specific questions: (i) Do the general CNN classifiers achieve comparable results to fine-grained classifiers? (ii) Do general CNN classifiers require any specific information to improve upon the fine-grained ones? Throughout this work, we train the general CNN classifiers without introducing any aspect that is specific to fine-grained datasets. We show an extensive evaluation on six datasets to determine whether the fine-grained classifier is able to elevate the baseline in their experiments.
