Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of Attention
Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of Attention
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发表时间：
2021-06
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通讯作者：
M. Keaton;Ram J. Zaveri;Meghana Kovur;Cole Henderson;D. Adjeroh;Gianfranco Doretto
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作者：
M. Keaton;Ram J. Zaveri;Meghana Kovur;Cole Henderson;D. Adjeroh;Gianfranco Doretto
在野外的植物物种识别是一个困难的问题，部分原因是由于输入数据的高度可变性，但也因为数据集分布的长尾效应引起的复杂性。受最近基于注意力的细粒度视觉分类方法的启发，我们探索了将对象检测作为注意力的一种形式的想法。我们介绍了一种自下而上的方法，基于检测植物器官和融合的预测变量数量的器官为基础的物种分类。我们还策划了一个具有长尾分布的新数据集，用于评估植物器官检测和基于器官的物种识别，该数据集是公开的。
Plant species identification in the wild is a difficult problem in part due to the high variability of the input data, but also because of complications induced by the long-tail effects of the datasets distribution. Inspired by the most recent fine-grained visual classification approaches which are based on attention to mitigate the effects of data variability, we explore the idea of using object detection as a form of attention. We introduce a bottom-up approach based on detecting plant organs and fusing the predictions of a variable number of organ-based species classifiers. We also curate a new dataset with a long-tail distribution for evaluating plant organ detection and organ-based species identification, which is publicly available.