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arXiv · 2106.02141

Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of Attention

Matthew R. Keaton·Ram J. Zaveri·Meghana Kovur·Cole Henderson·Donald A. Adjeroh·Gianfranco Doretto
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Abstract

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.

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Matthew R. Keaton, Ram J. Zaveri, Meghana Kovur, Cole Henderson, Donald A. Adjeroh, Gianfranco Doretto. 2021-06-03. Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of Attention. https://arxiv.org/abs/2106.02141

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